# load("vcomball20210902.Rda")
# d <- vcomball
load(path(here::here("InitalDataCleaning/Data/vsurvall20210902.Rda")))
d <- vsurvall
#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))
SITE ID
- Codes(based on Surveyid)
- 10 Greater CA
- 20 Georgia
- 25 North Carolina
- 30 Northern CA
- 40 Louisiana
- 50 New Jersey
- 60 Detroit
- 61 Michigan
- 70 Texas
- 80 Los Angeles County
- 81 USC-Other
- 82 USC-MEC
- 90 New York
- 94 Florida
- 95 WebRecruit-Limbo
- 99 WebRecruit
siteid <- as.factor(trimws(d[,"siteid"]))
#new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"
siteid_new<- siteid
d<-data.frame(d, siteid_new)
new.d <- data.frame(new.d, siteid)
new.d <- apply_labels(new.d, siteid = "Site ID")
new.d.1 <- data.frame(new.d.1, siteid)
siteid_count<-count(new.d$siteid)
colnames(siteid_count)<- c("Registry", "Total")
kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
Overview of all Registries
| Greater CA.10 |
409 |
| Georgia.20 |
2231 |
| Northern CA.30 |
258 |
| Louisiana.40 |
728 |
| New Jersey.50 |
383 |
| Detroit.60 |
474 |
| Michigan.61 |
115 |
| Texas.70 |
291 |
| Los Angeles County.80 |
463 |
| USC Other.81 |
207 |
| USC MEC.82 |
74 |
| WebRecruit.99 |
206 |
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]
SURVEY ID
- Scantron assigned SurveyID
surveyid <- as.factor(d[,"surveyid"])
isDup <- duplicated(surveyid)
numDups <- sum(isDup)
dups <- surveyid[isDup]
new.d <- data.frame(new.d, surveyid)
new.d <- apply_labels(new.d, surveyid = "Survey ID")
print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 11"
print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
print(dups)
## [1] 200312 201605 101079 211392 990607 300631 990384 209179 600984 100849 601287
## 5828 Levels: 100037 100050 100059 100061 100064 100072 100073 100078 100080 100084 100088 100092 ... 991829
print("Number of NAs:")
## [1] "Number of NAs:"
print(sum(is.na(new.d$surveyid)))
## [1] 0
LOCATION NAME
- Name of Registry delivery location
locationname <- as.factor(d[,"locationname"])
new.d <- data.frame(new.d, locationname)
new.d <- apply_labels(new.d, locationname = "Recruitment Location")
temp.d <- data.frame (new.d, locationname)
result<-questionr::freq(temp.d$locationname, total = TRUE)
#Create a NICE table
kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
| Detroit |
486 |
8.3 |
8.3 |
| Georgia |
2360 |
40.4 |
40.4 |
| Greater Bay |
258 |
4.4 |
4.4 |
| Greater California |
411 |
7.0 |
7.0 |
| Los Angeles |
463 |
7.9 |
7.9 |
| Louisiana |
599 |
10.3 |
10.3 |
| New Jersey |
383 |
6.6 |
6.6 |
| Texas |
291 |
5.0 |
5.0 |
| Virtual |
588 |
10.1 |
10.1 |
| Total |
5839 |
100.0 |
100.0 |
RESPOND ID
- From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
respondid <- as.factor(d[,"respondid"])
#remove NAs in respondid in order to avoid showing NAs in duplicated values
respondid_rm<-respondid[!is.na(respondid)]
isDup <- duplicated(respondid_rm)
numDups <- sum(isDup)
dups <- respondid_rm[isDup]
new.d <- data.frame(new.d, respondid)
new.d <- apply_labels(new.d, respondid = "RESPOND ID")
print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 9"
print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
print(dups)
## [1] 30100172 20100647 20100647 10101033 30100177 61100327 40102138 81100245 81100244
## 4676 Levels: 10100003 10100012 10100023 10100024 10100027 10100042 10100047 10100048 10100052 10100054 ... 99800213
print("Number of NAs:")
## [1] "Number of NAs:"
print(sum(is.na(new.d$respondid)))
## [1] 1154
METHODOLOGY
- How survey was completed
- P=Paper
- O=Online complete
st_css()
methodology <- as.factor(d[,"methodology"])
levels(methodology) <- list(Paper="P",
Online="O")
methodology <- ordered(methodology, c("Paper", "Online"))
new.d <- data.frame(new.d, methodology)
new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
temp.d <- data.frame (new.d, methodology)
result<-questionr::freq(temp.d$methodology, total = TRUE)
kable(result, format = "simple", align = 'l')
| Paper |
4728 |
81 |
81 |
| Online |
1111 |
19 |
19 |
| Total |
5839 |
100 |
100 |
A1: Date of diagnosis
- A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
new.d <- data.frame(new.d, a1month)
new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
temp.d <- data.frame (new.d, a1month)
result<-questionr::freq(temp.d$a1month, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
| 0 |
2 |
0.0 |
0.0 |
| 0* |
1 |
0.0 |
0.0 |
| 1 |
361 |
6.2 |
7.1 |
| 10 |
441 |
7.6 |
8.6 |
| 11 |
361 |
6.2 |
7.1 |
| 12 |
275 |
4.7 |
5.4 |
| 18 |
1 |
0.0 |
0.0 |
| 2 |
377 |
6.5 |
7.4 |
| 22 |
1 |
0.0 |
0.0 |
| 25 |
2 |
0.0 |
0.0 |
| 3 |
499 |
8.5 |
9.8 |
| 32 |
1 |
0.0 |
0.0 |
| 4 |
445 |
7.6 |
8.7 |
| 48 |
1 |
0.0 |
0.0 |
| 5 |
444 |
7.6 |
8.7 |
| 6 |
655 |
11.2 |
12.8 |
| 7 |
409 |
7.0 |
8.0 |
| 8 |
406 |
7.0 |
8.0 |
| 9 |
420 |
7.2 |
8.2 |
| NA |
737 |
12.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#count<-as.data.frame(table(new.d$a1month))
#colnames(count)<- c("a1month", "Total")
#freq1<-table(new.d$a1month)
#freq<-as.data.frame(round(prop.table(freq1),3))
#colnames(freq)<- c("a1month", "Freq")
#result<-merge(count, freq,by="a1month",sort=F)
#kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
#a1year
tmp<-d[,"a1year"]
tmp[tmp=="15"]<-"2015"
a1year <- as.factor(tmp)
#levels(a1year)[levels(a1year)=="15"] <- "2015"
#a1year[a1year=="15"] <- "2015" # change "15" to "2015"
#a1year <- as.Date(a1year, format = "%Y")
#a1year <- relevel(a1year, ref="1914")
new.d <- data.frame(new.d, a1year)
new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
temp.d <- data.frame (new.d, a1year)
result<-questionr::freq(temp.d$a1year, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
| 19 |
1 |
0.0 |
0.0 |
| 1914 |
1 |
0.0 |
0.0 |
| 1915 |
5 |
0.1 |
0.1 |
| 1916 |
9 |
0.2 |
0.2 |
| 1917 |
12 |
0.2 |
0.2 |
| 1918 |
3 |
0.1 |
0.1 |
| 1919 |
1 |
0.0 |
0.0 |
| 1920 |
1 |
0.0 |
0.0 |
| 1936 |
1 |
0.0 |
0.0 |
| 1937 |
1 |
0.0 |
0.0 |
| 1941 |
4 |
0.1 |
0.1 |
| 1943 |
2 |
0.0 |
0.0 |
| 1944 |
1 |
0.0 |
0.0 |
| 1945 |
2 |
0.0 |
0.0 |
| 1946 |
1 |
0.0 |
0.0 |
| 1947 |
2 |
0.0 |
0.0 |
| 1948 |
2 |
0.0 |
0.0 |
| 1949 |
3 |
0.1 |
0.1 |
| 1950 |
4 |
0.1 |
0.1 |
| 1951 |
3 |
0.1 |
0.1 |
| 1952 |
3 |
0.1 |
0.1 |
| 1953 |
1 |
0.0 |
0.0 |
| 1954 |
3 |
0.1 |
0.1 |
| 1955 |
2 |
0.0 |
0.0 |
| 1956 |
2 |
0.0 |
0.0 |
| 1957 |
1 |
0.0 |
0.0 |
| 1960 |
3 |
0.1 |
0.1 |
| 1963 |
2 |
0.0 |
0.0 |
| 1965 |
1 |
0.0 |
0.0 |
| 1980 |
1 |
0.0 |
0.0 |
| 1985 |
1 |
0.0 |
0.0 |
| 1987 |
1 |
0.0 |
0.0 |
| 1988 |
1 |
0.0 |
0.0 |
| 1989 |
1 |
0.0 |
0.0 |
| 1990 |
2 |
0.0 |
0.0 |
| 1992 |
1 |
0.0 |
0.0 |
| 1993 |
5 |
0.1 |
0.1 |
| 1994 |
2 |
0.0 |
0.0 |
| 1995 |
6 |
0.1 |
0.1 |
| 1996 |
3 |
0.1 |
0.1 |
| 1997 |
4 |
0.1 |
0.1 |
| 1998 |
9 |
0.2 |
0.2 |
| 1999 |
15 |
0.3 |
0.3 |
| 20 |
2 |
0.0 |
0.0 |
| 2000 |
8 |
0.1 |
0.1 |
| 2001 |
2 |
0.0 |
0.0 |
| 2002 |
2 |
0.0 |
0.0 |
| 2003 |
4 |
0.1 |
0.1 |
| 2004 |
14 |
0.2 |
0.3 |
| 2005 |
23 |
0.4 |
0.4 |
| 2006 |
8 |
0.1 |
0.1 |
| 2007 |
8 |
0.1 |
0.1 |
| 2008 |
14 |
0.2 |
0.3 |
| 2009 |
22 |
0.4 |
0.4 |
| 2010 |
75 |
1.3 |
1.4 |
| 2011 |
64 |
1.1 |
1.2 |
| 2012 |
97 |
1.7 |
1.8 |
| 2013 |
129 |
2.2 |
2.4 |
| 2014 |
267 |
4.6 |
5.0 |
| 2015 |
1139 |
19.5 |
21.2 |
| 2016 |
1593 |
27.3 |
29.6 |
| 2017 |
1089 |
18.7 |
20.3 |
| 2018 |
480 |
8.2 |
8.9 |
| 2019 |
141 |
2.4 |
2.6 |
| 2020 |
51 |
0.9 |
0.9 |
| 2021 |
13 |
0.2 |
0.2 |
| 3017 |
1 |
0.0 |
0.0 |
| 615 |
1 |
0.0 |
0.0 |
| 618 |
1 |
0.0 |
0.0 |
| NA |
462 |
7.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
a1not <- as.factor(d[,"a1not"])
levels(a1not) <- list(NEVER_had_ProstateCancer="1",
HAVE_had_ProstateCancer="2")
new.d <- data.frame(new.d, a1not)
new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
temp.d <- data.frame (new.d, a1not)
result<-questionr::freq(temp.d$a1not, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
| NEVER_had_ProstateCancer |
18 |
0.3 |
0.3 |
| HAVE_had_ProstateCancer |
5821 |
99.7 |
99.7 |
| Total |
5839 |
100.0 |
100.0 |
A2: Identify as AA
- A2. Do you identify as Black or African American?
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
Yes="2")
a2 <- ordered(a2, c("Yes","No"))
new.d <- data.frame(new.d, a2)
new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
temp.d <- data.frame (new.d, a2)
result<-questionr::freq(temp.d$a2, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A2")
A2
| Yes |
5389 |
92.3 |
99.7 |
| No |
18 |
0.3 |
0.3 |
| NA |
432 |
7.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A3: Black or African American group
- A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
- A3_1: 1=Black/African American
- A3_2: 1=Nigerian
- A3_3: 1=Jamaican
- A3_4: 1=Ethiopian
- A3_5: 1=Haitian
- A3_6: 1=Somali
- a3_7: 1=Guyanese
- A3_8: 1=Creole
- A3_9: 1=West Indian
- A3_10: 1=Caribbean
- A3_11: 1=White
- A3_12: 1=Asian/Asian American
- A3_13: 1=Native American or American Indian or Alaskan Native
- A3_14: 1=Middle Eastern or North African
- A3_15: 1=Native Hawaiian or Pacific Islander
- A3_16: 1=Hispanic
- A3_17: 1=Latino
- A3_18: 1=Spanish
- A3_19: 1=Mexican/Mexican American
- A3_20: 1=Salvadoran
- A3_21: 1=Puerto Rican
- A3_22: 1=Dominican
- A3_23: 1=Columbian
- A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
levels(a3_1) <- list(Black_African_American="1")
new.d <- data.frame(new.d, a3_1)
new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
temp.d <- data.frame (new.d, a3_1)
result<-questionr::freq(temp.d$a3_1, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
| Black_African_American |
5372 |
92 |
100 |
| NA |
467 |
8 |
NA |
| Total |
5839 |
100 |
100 |
a3_2 <- as.factor(d[,"a3_2"])
levels(a3_2) <- list(Nigerian="1")
new.d <- data.frame(new.d, a3_2)
new.d <- apply_labels(new.d, a3_2 = "Nigerian")
temp.d <- data.frame (new.d, a3_2)
result<-questionr::freq(temp.d$a3_2, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
| Nigerian |
122 |
2.1 |
100 |
| NA |
5717 |
97.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_3 <- as.factor(d[,"a3_3"])
levels(a3_3) <- list(Jamaican="1")
new.d <- data.frame(new.d, a3_3)
new.d <- apply_labels(new.d, a3_3 = "Jamaican")
temp.d <- data.frame (new.d, a3_3)
result<-questionr::freq(temp.d$a3_3, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
| Jamaican |
116 |
2 |
100 |
| NA |
5723 |
98 |
NA |
| Total |
5839 |
100 |
100 |
a3_4 <- as.factor(d[,"a3_4"])
levels(a3_4) <- list(Ethiopian="1")
new.d <- data.frame(new.d, a3_4)
new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
temp.d <- data.frame (new.d, a3_4)
result<-questionr::freq(temp.d$a3_4, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
| Ethiopian |
16 |
0.3 |
100 |
| NA |
5823 |
99.7 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_5 <- as.factor(d[,"a3_5"])
levels(a3_5) <- list(Haitian="1")
new.d <- data.frame(new.d, a3_5)
new.d <- apply_labels(new.d, a3_5 = "Haitian")
temp.d <- data.frame (new.d, a3_5)
result<-questionr::freq(temp.d$a3_5, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
| Haitian |
39 |
0.7 |
100 |
| NA |
5800 |
99.3 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_6 <- as.factor(d[,"a3_6"])
levels(a3_6) <- list(Somali="1")
new.d <- data.frame(new.d, a3_6)
new.d <- apply_labels(new.d, a3_6 = "Somali")
temp.d <- data.frame (new.d, a3_6)
result<-questionr::freq(temp.d$a3_6, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
| Somali |
3 |
0.1 |
100 |
| NA |
5836 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_7 <- as.factor(d[,"a3_7"])
levels(a3_7) <- list(Guyanese="1")
new.d <- data.frame(new.d, a3_7)
new.d <- apply_labels(new.d, a3_7 = "Guyanese")
temp.d <- data.frame (new.d, a3_7)
result<-questionr::freq(temp.d$a3_7, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
| Guyanese |
28 |
0.5 |
100 |
| NA |
5811 |
99.5 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_8 <- as.factor(d[,"a3_8"])
levels(a3_8) <- list(Creole="1")
new.d <- data.frame(new.d, a3_8)
new.d <- apply_labels(new.d, a3_8 = "Creole")
temp.d <- data.frame (new.d, a3_8)
result<-questionr::freq(temp.d$a3_8, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
| Creole |
69 |
1.2 |
100 |
| NA |
5770 |
98.8 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_9 <- as.factor(d[,"a3_9"])
levels(a3_9) <- list(West_Indian="1")
new.d <- data.frame(new.d, a3_9)
new.d <- apply_labels(new.d, a3_9 = "West_Indian")
temp.d <- data.frame (new.d, a3_9)
result<-questionr::freq(temp.d$a3_9, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
| West_Indian |
110 |
1.9 |
100 |
| NA |
5729 |
98.1 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_10 <- as.factor(d[,"a3_10"])
levels(a3_10) <- list(Caribbean="1")
new.d <- data.frame(new.d, a3_10)
new.d <- apply_labels(new.d, a3_10 = "Caribbean")
temp.d <- data.frame (new.d, a3_10)
result<-questionr::freq(temp.d$a3_10, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
| Caribbean |
104 |
1.8 |
100 |
| NA |
5735 |
98.2 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_11 <- as.factor(d[,"a3_11"])
levels(a3_11) <- list(White="1")
new.d <- data.frame(new.d, a3_11)
new.d <- apply_labels(new.d, a3_11 = "White")
temp.d <- data.frame (new.d, a3_11)
result<-questionr::freq(temp.d$a3_11, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
| White |
69 |
1.2 |
100 |
| NA |
5770 |
98.8 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_12 <- as.factor(d[,"a3_12"])
levels(a3_12) <- list(Asian="1")
new.d <- data.frame(new.d, a3_12)
new.d <- apply_labels(new.d, a3_12 = "Asian")
temp.d <- data.frame (new.d, a3_12)
result<-questionr::freq(temp.d$a3_12, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
| Asian |
10 |
0.2 |
100 |
| NA |
5829 |
99.8 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_13 <- as.factor(d[,"a3_13"])
levels(a3_13) <- list(Native_Indian="1")
new.d <- data.frame(new.d, a3_13)
new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
temp.d <- data.frame (new.d, a3_13)
result<-questionr::freq(temp.d$a3_13, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
| Native_Indian |
142 |
2.4 |
100 |
| NA |
5697 |
97.6 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_14 <- as.factor(d[,"a3_14"])
levels(a3_14) <- list(Middle_Eastern_North_African="1")
new.d <- data.frame(new.d, a3_14)
new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
temp.d <- data.frame (new.d, a3_14)
result<-questionr::freq(temp.d$a3_14, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
| Middle_Eastern_North_African |
7 |
0.1 |
100 |
| NA |
5832 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_15 <- as.factor(d[,"a3_15"])
levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
new.d <- data.frame(new.d, a3_15)
new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
temp.d <- data.frame (new.d, a3_15)
result<-questionr::freq(temp.d$a3_15, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
| Native_Hawaiian_Pacific_Islander |
7 |
0.1 |
100 |
| NA |
5832 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_16 <- as.factor(d[,"a3_16"])
levels(a3_16) <- list(Hispanic="1")
new.d <- data.frame(new.d, a3_16)
new.d <- apply_labels(new.d, a3_16 = "Hispanic")
temp.d <- data.frame (new.d, a3_16)
result<-questionr::freq(temp.d$a3_16, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
| Hispanic |
24 |
0.4 |
100 |
| NA |
5815 |
99.6 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_17 <- as.factor(d[,"a3_17"])
levels(a3_17) <- list(Latino="1")
new.d <- data.frame(new.d, a3_17)
new.d <- apply_labels(new.d, a3_17 = "Latino")
temp.d <- data.frame (new.d, a3_17)
result<-questionr::freq(temp.d$a3_17, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
| Latino |
16 |
0.3 |
100 |
| NA |
5823 |
99.7 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_18 <- as.factor(d[,"a3_18"])
levels(a3_18) <- list(Spanish="1")
new.d <- data.frame(new.d, a3_18)
new.d <- apply_labels(new.d, a3_18 = "Spanish")
temp.d <- data.frame (new.d, a3_18)
result<-questionr::freq(temp.d$a3_18, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
| Spanish |
9 |
0.2 |
100 |
| NA |
5830 |
99.8 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_19 <- as.factor(d[,"a3_19"])
levels(a3_19) <- list(Mexican="1")
new.d <- data.frame(new.d, a3_19)
new.d <- apply_labels(new.d, a3_19 = "Mexican")
temp.d <- data.frame (new.d, a3_19)
result<-questionr::freq(temp.d$a3_19, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
| Mexican |
6 |
0.1 |
100 |
| NA |
5833 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_20 <- as.factor(d[,"a3_20"])
levels(a3_20) <- list(Salvadoran="1")
new.d <- data.frame(new.d, a3_20)
new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
temp.d <- data.frame (new.d, a3_20)
result<-questionr::freq(temp.d$a3_20, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
| Salvadoran |
2 |
0 |
100 |
| NA |
5837 |
100 |
NA |
| Total |
5839 |
100 |
100 |
a3_21 <- as.factor(d[,"a3_21"])
levels(a3_21) <- list(Puerto_Rican="1")
new.d <- data.frame(new.d, a3_21)
new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
temp.d <- data.frame (new.d, a3_21)
result<-questionr::freq(temp.d$a3_21, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
| Puerto_Rican |
17 |
0.3 |
100 |
| NA |
5822 |
99.7 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_22 <- as.factor(d[,"a3_22"])
levels(a3_22) <- list(Dominican="1")
new.d <- data.frame(new.d, a3_22)
new.d <- apply_labels(new.d, a3_22 = "Dominican")
temp.d <- data.frame (new.d, a3_22)
result<-questionr::freq(temp.d$a3_22, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
| Dominican |
7 |
0.1 |
100 |
| NA |
5832 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_23 <- as.factor(d[,"a3_23"])
levels(a3_23) <- list(Columbian="1")
new.d <- data.frame(new.d, a3_23)
new.d <- apply_labels(new.d, a3_23 = "Columbian")
temp.d <- data.frame (new.d, a3_23)
result<-questionr::freq(temp.d$a3_23, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
| Columbian |
4 |
0.1 |
100 |
| NA |
5835 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
a3_24 <- as.factor(d[,"a3_24"])
levels(a3_23) <- list(Other="1")
new.d <- data.frame(new.d, a3_24)
new.d <- apply_labels(new.d, a3_24 = "Other")
temp.d <- data.frame (new.d, a3_24)
result<-questionr::freq(temp.d$a3_24, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "24. Other")
24. Other
| 1 |
94 |
1.6 |
100 |
| NA |
5745 |
98.4 |
NA |
| Total |
5839 |
100.0 |
100 |
A3 Other: Black or African American group
a3other <- d[,"a3other"]
new.d <- data.frame(new.d, a3other)
new.d <- apply_labels(new.d, a3other = "A3Other")
temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A3Other")
A3Other
| ???!!! |
1 |
0.0 |
0.7 |
| Aboriginal |
1 |
0.0 |
0.7 |
| African (Liberian) |
1 |
0.0 |
0.7 |
| All black American |
1 |
0.0 |
0.7 |
| American |
1 |
0.0 |
0.7 |
| American Black |
1 |
0.0 |
0.7 |
| american with african and european heritage |
1 |
0.0 |
0.7 |
| American-I know no other |
1 |
0.0 |
0.7 |
| American. |
1 |
0.0 |
0.7 |
| Asian Indian |
1 |
0.0 |
0.7 |
| Atlantic Islander, British, Bermuda |
1 |
0.0 |
0.7 |
| Bahamas |
1 |
0.0 |
0.7 |
| Barbadian |
1 |
0.0 |
0.7 |
| Barbadion My birth place |
1 |
0.0 |
0.7 |
| Barbados |
1 |
0.0 |
0.7 |
| Because my grandparents were mixed with Spanish and Native American and black. |
1 |
0.0 |
0.7 |
| Belize |
1 |
0.0 |
0.7 |
| Belize Central America, mother Hondurian |
1 |
0.0 |
0.7 |
| Belizean |
1 |
0.0 |
0.7 |
| Black |
1 |
0.0 |
0.7 |
| black american |
1 |
0.0 |
0.7 |
| Black British |
1 |
0.0 |
0.7 |
| Black Jew/Negro |
1 |
0.0 |
0.7 |
| Black Latino. |
1 |
0.0 |
0.7 |
| Black Man |
1 |
0.0 |
0.7 |
| Black, white , American Indian |
1 |
0.0 |
0.7 |
| Blackfoot Indian |
1 |
0.0 |
0.7 |
| Blended mix race |
1 |
0.0 |
0.7 |
| Born in St. Thomas US Virgin Island |
1 |
0.0 |
0.7 |
| Both African-American and Creole. |
1 |
0.0 |
0.7 |
| Cameroon |
1 |
0.0 |
0.7 |
| Cameroon, Congo |
1 |
0.0 |
0.7 |
| Cameroon, Congo (from ethnicity estimate) from DNA sample |
1 |
0.0 |
0.7 |
| Cameroonian |
1 |
0.0 |
0.7 |
| Cameroonian. |
2 |
0.0 |
1.5 |
| Cape Verdean |
1 |
0.0 |
0.7 |
| Chanaian |
1 |
0.0 |
0.7 |
| Congolese |
2 |
0.0 |
1.5 |
| Cuban |
2 |
0.0 |
1.5 |
| English |
1 |
0.0 |
0.7 |
| Father Panamanian |
1 |
0.0 |
0.7 |
| Fathers family |
1 |
0.0 |
0.7 |
| French |
1 |
0.0 |
0.7 |
| French and Black and Cherokee |
1 |
0.0 |
0.7 |
| From West Africa, Ivory Coast (French Speaking) |
1 |
0.0 |
0.7 |
| Gambian |
1 |
0.0 |
0.7 |
| German/Jewish |
1 |
0.0 |
0.7 |
| Ghana |
2 |
0.0 |
1.5 |
| Ghana. |
2 |
0.0 |
1.5 |
| Ghanaian |
8 |
0.1 |
6.0 |
| Ghanaian. |
6 |
0.1 |
4.5 |
| Ghanaian/US citizen |
1 |
0.0 |
0.7 |
| Ghanian |
1 |
0.0 |
0.7 |
| Great Grand Father Side (Kenyon) |
1 |
0.0 |
0.7 |
| Greek |
1 |
0.0 |
0.7 |
| Guatemalan |
1 |
0.0 |
0.7 |
| Guinea-CKY |
1 |
0.0 |
0.7 |
| Guyanese |
1 |
0.0 |
0.7 |
| Haitian |
1 |
0.0 |
0.7 |
| Hebrew Israelite Black |
1 |
0.0 |
0.7 |
| Hebrew Israelite. |
1 |
0.0 |
0.7 |
| Human race |
2 |
0.0 |
1.5 |
| I do have fore parents from the African continent I am, however I am not familiar with the name or country of origin unfamiliar |
1 |
0.0 |
0.7 |
| I’m biracial, Father-white, mother-black. |
1 |
0.0 |
0.7 |
| If you need to call me Larry D. Joyner 912-344-5895. |
1 |
0.0 |
0.7 |
| India/South Asia |
1 |
0.0 |
0.7 |
| Indigenous Moor. |
1 |
0.0 |
0.7 |
| Irish |
1 |
0.0 |
0.7 |
| Irish/Cape Verdian |
1 |
0.0 |
0.7 |
| Jewish |
2 |
0.0 |
1.5 |
| Kenyan |
2 |
0.0 |
1.5 |
| Liberian African |
1 |
0.0 |
0.7 |
| Liberian. |
1 |
0.0 |
0.7 |
| Louisiana Creole of color, French |
1 |
0.0 |
0.7 |
| Mixed Black/White |
1 |
0.0 |
0.7 |
| Mixed race - White & American Black |
1 |
0.0 |
0.7 |
| Moorish National Indigenous to |
1 |
0.0 |
0.7 |
| Mother 1/2 White |
1 |
0.0 |
0.7 |
| Mother is white and Father was Black |
1 |
0.0 |
0.7 |
| Mulatto, Latino and African |
1 |
0.0 |
0.7 |
| My choice would be American |
1 |
0.0 |
0.7 |
| My father dad was Jewish |
1 |
0.0 |
0.7 |
| Negro |
3 |
0.1 |
2.2 |
| None. |
2 |
0.0 |
1.5 |
| of mixed heritage |
1 |
0.0 |
0.7 |
| Panama is also a country! |
1 |
0.0 |
0.7 |
| Panamanian |
1 |
0.0 |
0.7 |
| Panamanian/Costa Rican |
1 |
0.0 |
0.7 |
| Parents of color Jamaican father, mother Cuban |
1 |
0.0 |
0.7 |
| Rwandan |
1 |
0.0 |
0.7 |
| Sex problems and smaller penis |
1 |
0.0 |
0.7 |
| Sierra Leone |
1 |
0.0 |
0.7 |
| Sierra Leonean |
2 |
0.0 |
1.5 |
| Sierra Leonean, West Africa |
1 |
0.0 |
0.7 |
| Sierre Leone |
1 |
0.0 |
0.7 |
| Surnam African American. |
1 |
0.0 |
0.7 |
| Tanzanian |
1 |
0.0 |
0.7 |
| Togo |
1 |
0.0 |
0.7 |
| Togolese |
1 |
0.0 |
0.7 |
| Trinidad |
1 |
0.0 |
0.7 |
| Trinidad and Tobago. |
1 |
0.0 |
0.7 |
| Trinidadian |
1 |
0.0 |
0.7 |
| Trinidadian. |
1 |
0.0 |
0.7 |
| Uganda |
1 |
0.0 |
0.7 |
| Venezuela |
1 |
0.0 |
0.7 |
| Virgin Islands |
1 |
0.0 |
0.7 |
| West Africa Liberia |
1 |
0.0 |
0.7 |
| West African |
3 |
0.1 |
2.2 |
| NA |
5705 |
97.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A4: Month and year of birth
A4. What is your month and year of birth?
# a4month
a4month <- as.factor(d[,"a4month"])
new.d <- data.frame(new.d, a4month)
new.d <- apply_labels(new.d, a4month = "Month of birth")
temp.d <- data.frame (new.d, a4month)
result<-questionr::freq(temp.d$a4month, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
| 1 |
486 |
8.3 |
8.4 |
| 10 |
515 |
8.8 |
8.9 |
| 11 |
478 |
8.2 |
8.2 |
| 12 |
520 |
8.9 |
9.0 |
| 18 |
1 |
0.0 |
0.0 |
| 2 |
441 |
7.6 |
7.6 |
| 22 |
1 |
0.0 |
0.0 |
| 24 |
2 |
0.0 |
0.0 |
| 25 |
1 |
0.0 |
0.0 |
| 26 |
1 |
0.0 |
0.0 |
| 3 |
451 |
7.7 |
7.8 |
| 31 |
1 |
0.0 |
0.0 |
| 33 |
1 |
0.0 |
0.0 |
| 35 |
1 |
0.0 |
0.0 |
| 4 |
417 |
7.1 |
7.2 |
| 41 |
2 |
0.0 |
0.0 |
| 42 |
1 |
0.0 |
0.0 |
| 48 |
1 |
0.0 |
0.0 |
| 5 |
444 |
7.6 |
7.6 |
| 57 |
1 |
0.0 |
0.0 |
| 58 |
1 |
0.0 |
0.0 |
| 6 |
463 |
7.9 |
8.0 |
| 61 |
2 |
0.0 |
0.0 |
| 7 |
516 |
8.8 |
8.9 |
| 71 |
2 |
0.0 |
0.0 |
| 8 |
555 |
9.5 |
9.6 |
| 84 |
1 |
0.0 |
0.0 |
| 9 |
503 |
8.6 |
8.7 |
| 96 |
1 |
0.0 |
0.0 |
| NA |
29 |
0.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#a4year
a4year <- as.factor(d[,"a4year"])
new.d <- data.frame(new.d, a4year)
new.d <- apply_labels(new.d, a4year = "Year of birth")
temp.d <- data.frame (new.d, a4year)
result<-questionr::freq(temp.d$a4year, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
| 1062 |
1 |
0.0 |
0.0 |
| 1340 |
1 |
0.0 |
0.0 |
| 1923 |
1 |
0.0 |
0.0 |
| 1924 |
2 |
0.0 |
0.0 |
| 1925 |
1 |
0.0 |
0.0 |
| 1927 |
1 |
0.0 |
0.0 |
| 1928 |
2 |
0.0 |
0.0 |
| 1929 |
1 |
0.0 |
0.0 |
| 1930 |
1 |
0.0 |
0.0 |
| 1931 |
2 |
0.0 |
0.0 |
| 1932 |
5 |
0.1 |
0.1 |
| 1933 |
5 |
0.1 |
0.1 |
| 1934 |
5 |
0.1 |
0.1 |
| 1935 |
8 |
0.1 |
0.1 |
| 1936 |
9 |
0.2 |
0.2 |
| 1937 |
19 |
0.3 |
0.3 |
| 1938 |
24 |
0.4 |
0.4 |
| 1939 |
31 |
0.5 |
0.5 |
| 1940 |
43 |
0.7 |
0.7 |
| 1941 |
90 |
1.5 |
1.5 |
| 1942 |
108 |
1.8 |
1.9 |
| 1943 |
145 |
2.5 |
2.5 |
| 1944 |
153 |
2.6 |
2.6 |
| 1945 |
170 |
2.9 |
2.9 |
| 1946 |
237 |
4.1 |
4.1 |
| 1947 |
240 |
4.1 |
4.1 |
| 1948 |
306 |
5.2 |
5.3 |
| 1949 |
299 |
5.1 |
5.1 |
| 1950 |
320 |
5.5 |
5.5 |
| 1951 |
322 |
5.5 |
5.5 |
| 1952 |
292 |
5.0 |
5.0 |
| 1953 |
282 |
4.8 |
4.8 |
| 1954 |
293 |
5.0 |
5.0 |
| 1955 |
295 |
5.1 |
5.1 |
| 1956 |
299 |
5.1 |
5.1 |
| 1957 |
277 |
4.7 |
4.8 |
| 1958 |
235 |
4.0 |
4.0 |
| 1959 |
192 |
3.3 |
3.3 |
| 1960 |
222 |
3.8 |
3.8 |
| 1961 |
171 |
2.9 |
2.9 |
| 1962 |
130 |
2.2 |
2.2 |
| 1963 |
128 |
2.2 |
2.2 |
| 1964 |
94 |
1.6 |
1.6 |
| 1965 |
75 |
1.3 |
1.3 |
| 1966 |
68 |
1.2 |
1.2 |
| 1967 |
55 |
0.9 |
0.9 |
| 1968 |
45 |
0.8 |
0.8 |
| 1969 |
37 |
0.6 |
0.6 |
| 1970 |
17 |
0.3 |
0.3 |
| 1971 |
12 |
0.2 |
0.2 |
| 1972 |
10 |
0.2 |
0.2 |
| 1973 |
15 |
0.3 |
0.3 |
| 1974 |
3 |
0.1 |
0.1 |
| 1975 |
3 |
0.1 |
0.1 |
| 1976 |
2 |
0.0 |
0.0 |
| 1977 |
2 |
0.0 |
0.0 |
| 1978 |
1 |
0.0 |
0.0 |
| 1981 |
1 |
0.0 |
0.0 |
| 2015 |
3 |
0.1 |
0.1 |
| 2016 |
1 |
0.0 |
0.0 |
| 2018 |
2 |
0.0 |
0.0 |
| 2582 |
1 |
0.0 |
0.0 |
| 2819 |
1 |
0.0 |
0.0 |
| 3042 |
1 |
0.0 |
0.0 |
| 663 |
1 |
0.0 |
0.0 |
| 748 |
1 |
0.0 |
0.0 |
| NA |
20 |
0.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A5: Where were you born
- A5. Where were you born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
Africa="2",
Cuba_Caribbean= "3",
Other="4")
a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
new.d <- data.frame(new.d, a5)
new.d <- apply_labels(new.d, a5 = "Born place")
temp.d <- data.frame (new.d, a5)
result<-questionr::freq(temp.d$a5, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
| US |
5369 |
92.0 |
92.5 |
| Africa |
181 |
3.1 |
3.1 |
| Cuba_Caribbean |
156 |
2.7 |
2.7 |
| Other |
99 |
1.7 |
1.7 |
| NA |
34 |
0.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A5 Other: Where were you born
a5other <- d[,"a5other"]
new.d <- data.frame(new.d, a5other)
new.d <- apply_labels(new.d, a5other = "a5other")
temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A5Other")
A5Other
| Achtal, Bavaria, Germany |
1 |
0.0 |
0.7 |
| Augsburg, Germany |
1 |
0.0 |
0.7 |
| Barbados |
2 |
0.0 |
1.3 |
| Beaumont Texas |
1 |
0.0 |
0.7 |
| Belize |
1 |
0.0 |
0.7 |
| Belize Central America |
1 |
0.0 |
0.7 |
| Belize, Central America |
2 |
0.0 |
1.3 |
| Belize, Toledo District |
1 |
0.0 |
0.7 |
| Bermuda Island |
1 |
0.0 |
0.7 |
| British Guiana, South America |
1 |
0.0 |
0.7 |
| Buffalo NY |
1 |
0.0 |
0.7 |
| Central America (Panama) |
1 |
0.0 |
0.7 |
| Central America Belize |
1 |
0.0 |
0.7 |
| Chatroux, France |
1 |
0.0 |
0.7 |
| Chicago IL |
1 |
0.0 |
0.7 |
| Colon Panama |
1 |
0.0 |
0.7 |
| Costa Rica |
1 |
0.0 |
0.7 |
| Detroit |
1 |
0.0 |
0.7 |
| Dominican Republic. |
1 |
0.0 |
0.7 |
| England |
1 |
0.0 |
0.7 |
| England. |
1 |
0.0 |
0.7 |
| Ethiopia |
1 |
0.0 |
0.7 |
| France |
1 |
0.0 |
0.7 |
| Frankfort, Germany |
1 |
0.0 |
0.7 |
| Georgetown Guyana |
1 |
0.0 |
0.7 |
| Georgia Monroe Walton County |
1 |
0.0 |
0.7 |
| Georgia. |
1 |
0.0 |
0.7 |
| Germany |
2 |
0.0 |
1.3 |
| Ghana |
3 |
0.1 |
2.0 |
| Ghana Elmina West Africa |
1 |
0.0 |
0.7 |
| Ghana. |
1 |
0.0 |
0.7 |
| Guatemala |
1 |
0.0 |
0.7 |
| Guinea-CKY |
1 |
0.0 |
0.7 |
| Guyana |
10 |
0.2 |
6.6 |
| Guyana, South America |
3 |
0.1 |
2.0 |
| Guyana, South America. |
1 |
0.0 |
0.7 |
| Guyana: South America |
1 |
0.0 |
0.7 |
| Guyana. |
2 |
0.0 |
1.3 |
| Haiti |
12 |
0.2 |
7.9 |
| Haiti-P-au-P. |
1 |
0.0 |
0.7 |
| Haiti. |
5 |
0.1 |
3.3 |
| Heidleburg Germany |
1 |
0.0 |
0.7 |
| Honduras CA |
1 |
0.0 |
0.7 |
| Houston |
1 |
0.0 |
0.7 |
| Jackson Mississippi |
1 |
0.0 |
0.7 |
| Jackson, Miss. |
1 |
0.0 |
0.7 |
| Jamaica |
11 |
0.2 |
7.2 |
| JAMAICA |
1 |
0.0 |
0.7 |
| Jamaica W.I. |
1 |
0.0 |
0.7 |
| Jamaica West Indies |
1 |
0.0 |
0.7 |
| Jamaica WI |
1 |
0.0 |
0.7 |
| Jamaica, W.I.. |
1 |
0.0 |
0.7 |
| Jamaica, WI. |
1 |
0.0 |
0.7 |
| Jamaica. |
1 |
0.0 |
0.7 |
| Jamaican |
1 |
0.0 |
0.7 |
| Japan |
1 |
0.0 |
0.7 |
| Kientra, Morocco |
1 |
0.0 |
0.7 |
| Kingston, Jamaica |
1 |
0.0 |
0.7 |
| Kingston, Jamaica W.I.. |
1 |
0.0 |
0.7 |
| Kingston, Jamaica. |
1 |
0.0 |
0.7 |
| Liberia |
1 |
0.0 |
0.7 |
| London England |
1 |
0.0 |
0.7 |
| Macon County |
1 |
0.0 |
0.7 |
| Mississippi |
1 |
0.0 |
0.7 |
| Nassau Bahamas |
2 |
0.0 |
1.3 |
| New Orleans, LA |
1 |
0.0 |
0.7 |
| Nigeria |
2 |
0.0 |
1.3 |
| Nigeria. |
1 |
0.0 |
0.7 |
| P-Au-P, Haiti |
1 |
0.0 |
0.7 |
| Palm Beach Fla |
1 |
0.0 |
0.7 |
| Panama |
4 |
0.1 |
2.6 |
| Panama City of Panama |
1 |
0.0 |
0.7 |
| Panama City Panama |
1 |
0.0 |
0.7 |
| Panama Rep Panama |
1 |
0.0 |
0.7 |
| Panama. |
2 |
0.0 |
1.3 |
| Phila PA |
1 |
0.0 |
0.7 |
| Puerto Rico. |
1 |
0.0 |
0.7 |
| Rep of Panama. |
1 |
0.0 |
0.7 |
| Republic of Guyana. |
1 |
0.0 |
0.7 |
| San Diego, CA |
1 |
0.0 |
0.7 |
| SC |
1 |
0.0 |
0.7 |
| Sierre Leone |
1 |
0.0 |
0.7 |
| South America, Guyana |
1 |
0.0 |
0.7 |
| South Carolina |
1 |
0.0 |
0.7 |
| Southampton, Bermuda |
1 |
0.0 |
0.7 |
| St. Lucia |
1 |
0.0 |
0.7 |
| St. Vincent and the Grenadines. |
1 |
0.0 |
0.7 |
| Suriname-Dutch Guyana. |
1 |
0.0 |
0.7 |
| tokyo japan |
1 |
0.0 |
0.7 |
| Trinidad |
2 |
0.0 |
1.3 |
| Trinidad and Tobago |
3 |
0.1 |
2.0 |
| Trinidad. |
2 |
0.0 |
1.3 |
| UK |
1 |
0.0 |
0.7 |
| United Kingdom |
1 |
0.0 |
0.7 |
| United States Texas |
1 |
0.0 |
0.7 |
| United States. |
1 |
0.0 |
0.7 |
| Upson County, GA |
1 |
0.0 |
0.7 |
| Venezuela |
1 |
0.0 |
0.7 |
| Venezuelan |
1 |
0.0 |
0.7 |
| West Indies |
1 |
0.0 |
0.7 |
| NA |
5687 |
97.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A6: Biological father born
- A6. Where was your biological father born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
Africa="2",
Cuba_Caribbean= "3",
Other="4")
a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
new.d <- data.frame(new.d, a6)
new.d <- apply_labels(new.d, a6 = "Born place")
temp.d <- data.frame (new.d, a6)
result<-questionr::freq(temp.d$a6, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
| US |
5286 |
90.5 |
91.5 |
| Africa |
183 |
3.1 |
3.2 |
| Cuba_Caribbean |
188 |
3.2 |
3.3 |
| Other |
117 |
2.0 |
2.0 |
| NA |
65 |
1.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A6 Other: Biological father born
a6other <- d[,"a6other"]
new.d <- data.frame(new.d, a6other)
new.d <- apply_labels(new.d, a6other = "a6other")
temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A6Other")
A6Other
| Bangladesh |
1 |
0.0 |
0.6 |
| Barbados |
4 |
0.1 |
2.5 |
| Belize |
1 |
0.0 |
0.6 |
| Belize Central America |
2 |
0.0 |
1.2 |
| Belize, Central America |
2 |
0.0 |
1.2 |
| Bermuda Island |
1 |
0.0 |
0.6 |
| Biological father unknown |
1 |
0.0 |
0.6 |
| Blackstone, VA |
1 |
0.0 |
0.6 |
| Bombay. |
1 |
0.0 |
0.6 |
| British Guiana |
1 |
0.0 |
0.6 |
| British Honduras. |
1 |
0.0 |
0.6 |
| Canada |
1 |
0.0 |
0.6 |
| Canada. |
1 |
0.0 |
0.6 |
| Castries St. Lucia |
1 |
0.0 |
0.6 |
| Central America (Panama) |
1 |
0.0 |
0.6 |
| Charleston South Carolina |
1 |
0.0 |
0.6 |
| Cleveland Mississippi |
1 |
0.0 |
0.6 |
| Costa Rica |
1 |
0.0 |
0.6 |
| Dead |
1 |
0.0 |
0.6 |
| Dominican Republic. |
1 |
0.0 |
0.6 |
| Don’t know |
2 |
0.0 |
1.2 |
| Don’t know biological father |
1 |
0.0 |
0.6 |
| Don’t know who father is |
1 |
0.0 |
0.6 |
| East Pakistan |
1 |
0.0 |
0.6 |
| England |
1 |
0.0 |
0.6 |
| Ethiopia |
1 |
0.0 |
0.6 |
| Father’s birthplace is unknown. |
1 |
0.0 |
0.6 |
| Georgia Monroe Walton County |
1 |
0.0 |
0.6 |
| Georgia. |
1 |
0.0 |
0.6 |
| Germany |
1 |
0.0 |
0.6 |
| Ghana |
2 |
0.0 |
1.2 |
| Ghana Elmina West Africa |
1 |
0.0 |
0.6 |
| Granada |
1 |
0.0 |
0.6 |
| Guatemala |
1 |
0.0 |
0.6 |
| Guyana |
10 |
0.2 |
6.2 |
| Guyana, South America |
3 |
0.1 |
1.9 |
| Guyana, South America. |
1 |
0.0 |
0.6 |
| Guyana: South America |
1 |
0.0 |
0.6 |
| Guyana. |
2 |
0.0 |
1.2 |
| Haiti |
12 |
0.2 |
7.4 |
| Haiti. |
4 |
0.1 |
2.5 |
| Honduras CA |
1 |
0.0 |
0.6 |
| Honduras, Central America |
1 |
0.0 |
0.6 |
| I don’t know |
1 |
0.0 |
0.6 |
| I was adopted, no info |
1 |
0.0 |
0.6 |
| Jackson Mississippi |
1 |
0.0 |
0.6 |
| Jamaica |
12 |
0.2 |
7.4 |
| JAMAICA |
1 |
0.0 |
0.6 |
| Jamaica W.I. |
1 |
0.0 |
0.6 |
| Jamaica West Indies |
1 |
0.0 |
0.6 |
| Jamaica WI |
2 |
0.0 |
1.2 |
| Jamaica, WI. |
1 |
0.0 |
0.6 |
| Jamaica. |
2 |
0.0 |
1.2 |
| Jamaica. W.I.. |
1 |
0.0 |
0.6 |
| Jamaican |
1 |
0.0 |
0.6 |
| Kingston, Jamaica, W.I.. |
1 |
0.0 |
0.6 |
| Kingston, Jamaica. |
1 |
0.0 |
0.6 |
| Limon, Costa Rica |
1 |
0.0 |
0.6 |
| Macon County |
1 |
0.0 |
0.6 |
| Marshall Texas |
1 |
0.0 |
0.6 |
| Mississippi |
2 |
0.0 |
1.2 |
| Montserrat British VI |
1 |
0.0 |
0.6 |
| Nassau Bahamas |
2 |
0.0 |
1.2 |
| Never knew my father |
1 |
0.0 |
0.6 |
| Nigeria |
2 |
0.0 |
1.2 |
| Nigeria. |
1 |
0.0 |
0.6 |
| Not known |
1 |
0.0 |
0.6 |
| not sure |
1 |
0.0 |
0.6 |
| P-Au-P, Haiti |
1 |
0.0 |
0.6 |
| Panama |
4 |
0.1 |
2.5 |
| Panama Canal Zone |
1 |
0.0 |
0.6 |
| Panama City of Panama |
1 |
0.0 |
0.6 |
| Panama City Panama |
1 |
0.0 |
0.6 |
| Panama Rep Panama |
1 |
0.0 |
0.6 |
| Panama-Central America |
1 |
0.0 |
0.6 |
| Panama. |
2 |
0.0 |
1.2 |
| Phila PA |
1 |
0.0 |
0.6 |
| Port-au-Prince, Haiti. |
1 |
0.0 |
0.6 |
| Possible Puerto Rico |
1 |
0.0 |
0.6 |
| Puerto Rico |
1 |
0.0 |
0.6 |
| Rep of Panama. |
1 |
0.0 |
0.6 |
| Republic of Guyana. |
1 |
0.0 |
0.6 |
| SC |
1 |
0.0 |
0.6 |
| Sierre Leone |
1 |
0.0 |
0.6 |
| Singapore. |
1 |
0.0 |
0.6 |
| South America, Guyana |
1 |
0.0 |
0.6 |
| St. Louis, MO |
1 |
0.0 |
0.6 |
| St. Vincent and the Grenadines. |
1 |
0.0 |
0.6 |
| St. Vincent. |
1 |
0.0 |
0.6 |
| Suriname-Dutch Guyana. |
1 |
0.0 |
0.6 |
| Trinidad |
2 |
0.0 |
1.2 |
| Trinidad and Tabago |
1 |
0.0 |
0.6 |
| Trinidad and Tobago |
2 |
0.0 |
1.2 |
| Trinidad. |
1 |
0.0 |
0.6 |
| United S Texas |
1 |
0.0 |
0.6 |
| United States. |
1 |
0.0 |
0.6 |
| unknown |
2 |
0.0 |
1.2 |
| Unknown |
5 |
0.1 |
3.1 |
| Venezuela |
1 |
0.0 |
0.6 |
| Vieques, Puerto Rico. |
1 |
0.0 |
0.6 |
| west Indies |
1 |
0.0 |
0.6 |
| Yatesville GA |
1 |
0.0 |
0.6 |
| NA |
5677 |
97.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A7: Biological mother born
- A7. Where was your biological mother born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
Africa="2",
Cuba_Caribbean= "3",
Other="4")
a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
new.d <- data.frame(new.d, a7)
new.d <- apply_labels(new.d, a7 = "Born place")
temp.d <- data.frame (new.d, a7)
result<-questionr::freq(temp.d$a7, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
| US |
5328 |
91.2 |
92.0 |
| Africa |
180 |
3.1 |
3.1 |
| Cuba_Caribbean |
184 |
3.2 |
3.2 |
| Other |
100 |
1.7 |
1.7 |
| NA |
47 |
0.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A7 Other: Biological father born
a7other <- d[,"a7other"]
new.d <- data.frame(new.d, a7other)
new.d <- apply_labels(new.d, a7other = "a7other")
temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A7Other")
A7Other
| Achtal, Bavaria, Germany |
1 |
0.0 |
0.7 |
| Anse-a-veau, Haiti |
1 |
0.0 |
0.7 |
| Atlanta, GA |
1 |
0.0 |
0.7 |
| Barbados |
2 |
0.0 |
1.4 |
| Belize |
1 |
0.0 |
0.7 |
| Belize Central America |
1 |
0.0 |
0.7 |
| Belize, Central America |
2 |
0.0 |
1.4 |
| Bermuda Island |
1 |
0.0 |
0.7 |
| Bremont, TX |
1 |
0.0 |
0.7 |
| British Guiana |
1 |
0.0 |
0.7 |
| Buffalo NY |
1 |
0.0 |
0.7 |
| Canada. |
2 |
0.0 |
1.4 |
| Central America (Colon, Panama) |
1 |
0.0 |
0.7 |
| Colombia |
1 |
0.0 |
0.7 |
| Costa rica |
1 |
0.0 |
0.7 |
| Costa Rica |
2 |
0.0 |
1.4 |
| Dominican Republic. |
1 |
0.0 |
0.7 |
| England |
1 |
0.0 |
0.7 |
| Ethiopia |
1 |
0.0 |
0.7 |
| Georgia Good Hope Walton County |
1 |
0.0 |
0.7 |
| Georgia. |
1 |
0.0 |
0.7 |
| Germany. |
2 |
0.0 |
1.4 |
| Ghana |
2 |
0.0 |
1.4 |
| Ghana Elmina West Africa |
1 |
0.0 |
0.7 |
| Grenada |
1 |
0.0 |
0.7 |
| Guatemala |
1 |
0.0 |
0.7 |
| guyana |
1 |
0.0 |
0.7 |
| Guyana |
11 |
0.2 |
7.6 |
| Guyana, South America |
3 |
0.1 |
2.1 |
| Guyana, South America. |
1 |
0.0 |
0.7 |
| Guyana: South America |
1 |
0.0 |
0.7 |
| Guyana. |
2 |
0.0 |
1.4 |
| Haiti |
12 |
0.2 |
8.3 |
| Haiti. |
4 |
0.1 |
2.8 |
| Honduras CA |
1 |
0.0 |
0.7 |
| Jackson Mississippi |
1 |
0.0 |
0.7 |
| Jamaica |
13 |
0.2 |
9.0 |
| JAMAICA |
1 |
0.0 |
0.7 |
| Jamaica BWI Kingston |
1 |
0.0 |
0.7 |
| Jamaica W.I. |
1 |
0.0 |
0.7 |
| Jamaica WI |
2 |
0.0 |
1.4 |
| Jamaica, W.I.. |
1 |
0.0 |
0.7 |
| Jamaica, WI. |
1 |
0.0 |
0.7 |
| Jamaica. |
1 |
0.0 |
0.7 |
| Japan |
3 |
0.1 |
2.1 |
| Kingston, Jamaica, W.I.. |
1 |
0.0 |
0.7 |
| Kingston, Jamaica. |
1 |
0.0 |
0.7 |
| LA |
1 |
0.0 |
0.7 |
| Lamar County, GA |
1 |
0.0 |
0.7 |
| Litchfield Guyana |
1 |
0.0 |
0.7 |
| Little Rock Arkansas |
1 |
0.0 |
0.7 |
| Macon County |
1 |
0.0 |
0.7 |
| Mexico |
1 |
0.0 |
0.7 |
| Mirebalais, Haiti. |
1 |
0.0 |
0.7 |
| Mississippi |
2 |
0.0 |
1.4 |
| Montserrat British VI |
1 |
0.0 |
0.7 |
| Nassau Bahamas |
1 |
0.0 |
0.7 |
| Nigeria |
2 |
0.0 |
1.4 |
| Nigeria. |
1 |
0.0 |
0.7 |
| No biological info, I was adopted. |
1 |
0.0 |
0.7 |
| Oklahoma |
1 |
0.0 |
0.7 |
| Panama |
5 |
0.1 |
3.4 |
| Panama City of Panama |
1 |
0.0 |
0.7 |
| Panama City Panama |
1 |
0.0 |
0.7 |
| Panama Rep Panama |
1 |
0.0 |
0.7 |
| Panama. |
2 |
0.0 |
1.4 |
| Phila PA |
1 |
0.0 |
0.7 |
| Puerto Rican |
1 |
0.0 |
0.7 |
| Puerto Rico. |
1 |
0.0 |
0.7 |
| Republic of Guyana. |
1 |
0.0 |
0.7 |
| SC |
1 |
0.0 |
0.7 |
| Sierre Leone |
1 |
0.0 |
0.7 |
| South America, Guyana |
1 |
0.0 |
0.7 |
| Spanish Honduras |
1 |
0.0 |
0.7 |
| St. Lucia |
1 |
0.0 |
0.7 |
| St. Vincent and the Grenadines. |
1 |
0.0 |
0.7 |
| Suriname-Dutch Guyana. |
1 |
0.0 |
0.7 |
| Trinidad |
1 |
0.0 |
0.7 |
| Trinidad and Tobago |
3 |
0.1 |
2.1 |
| Trinidad West Indies |
1 |
0.0 |
0.7 |
| Trinidad. |
2 |
0.0 |
1.4 |
| United States Texas |
1 |
0.0 |
0.7 |
| United States. |
1 |
0.0 |
0.7 |
| Venezuela |
2 |
0.0 |
1.4 |
| Vieques, Puerto Rico. |
1 |
0.0 |
0.7 |
| west Indies |
1 |
0.0 |
0.7 |
| NA |
5694 |
97.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
A8: Years lived in the US
- A8. How many years have you lived in the United States?
- 1=15 years or less
- 2=16-25 years
- 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
years_16_25="2",
more_than_25_or_whole_life= "3")
a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
new.d <- data.frame(new.d, a8)
new.d <- apply_labels(new.d, a8 = "Years lived in the US")
temp.d <- data.frame (new.d, a8)
result<-questionr::freq(temp.d$a8, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A8")
A8
| less_or_15 |
45 |
0.8 |
0.8 |
| years_16_25 |
90 |
1.5 |
1.6 |
| more_than_25_or_whole_life |
5580 |
95.6 |
97.6 |
| NA |
124 |
2.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B1A: Father
- B1Aa: Father: Has this person had prostate cancer?
- B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
- B1Ac: Father: Did he (or any) die of prostate cancer?
# B1Aa: Father: Has this person had prostate cancer?
b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
levels(b1aa) <- list(No="1",
Yes="2",
Dont_know="88")
b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1aa)
new.d <- apply_labels(new.d, b1aa = "Father")
temp.d <- data.frame (new.d, b1aa)
result<-questionr::freq(temp.d$b1aa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
| No |
3207 |
54.9 |
57.3 |
| Yes |
1105 |
18.9 |
19.7 |
| Dont_know |
1283 |
22.0 |
22.9 |
| NA |
244 |
4.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
levels(b1ab) <- list(No="1",
Yes="2",
Dont_know="88")
b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ab)
new.d <- apply_labels(new.d, b1ab = "Father")
temp.d <- data.frame (new.d, b1ab)
result<-questionr::freq(temp.d$b1ab,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
| No |
1122 |
19.2 |
58.9 |
| Yes |
130 |
2.2 |
6.8 |
| Dont_know |
652 |
11.2 |
34.2 |
| NA |
3935 |
67.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Ac: Father: Did he (or any) die of prostate cancer?
b1ac <- as.factor(d[,"b1ac"])
# Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
levels(b1ac) <- list(No="1",
Yes="2",
Dont_know="88")
b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ac)
new.d <- apply_labels(new.d, b1ac = "Father")
temp.d <- data.frame (new.d, b1ac)
result<-questionr::freq(temp.d$b1ac,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
| No |
1235 |
21.2 |
63.6 |
| Yes |
370 |
6.3 |
19.1 |
| Dont_know |
336 |
5.8 |
17.3 |
| NA |
3898 |
66.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B1B: Any Brother
- B1BNo: Any Brother
- 1=I had no brothers
- if not marked
- B1Ba: Any Brother: Has this person had prostate cancer?
- B1Ba2: Any Brother: If Yes, number with prostate cancer
- B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
- B1Bc: Any Brother: Did he (or any) die of prostate cancer?
# B1BNo: Any Brother
b1bno <- as.factor(d[,"b1bno"])
levels(b1bno) <- list(No_brothers="1")
new.d <- data.frame(new.d, b1bno)
new.d <- apply_labels(new.d, b1bno = "Any Brother")
temp.d <- data.frame (new.d, b1bno)
result<-questionr::freq(temp.d$b1bno,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
| No_brothers |
608 |
10.4 |
100 |
| NA |
5231 |
89.6 |
NA |
| Total |
5839 |
100.0 |
100 |
#B1Ba: Any Brother: Has this person had prostate cancer?
b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
levels(b1ba) <- list(No="1",
Yes="2",
Dont_know="88")
b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ba)
new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
temp.d <- data.frame (new.d, b1ba)
result<-questionr::freq(temp.d$b1ba,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
| No |
3410 |
58.4 |
66.6 |
| Yes |
1152 |
19.7 |
22.5 |
| Dont_know |
560 |
9.6 |
10.9 |
| NA |
717 |
12.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Ba2: Any Brother: If Yes, number with prostate cancer
b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
levels(b1ba2) <- list(One="1",
Two_or_more="2")
b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
new.d <- data.frame(new.d, b1ba2)
new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
temp.d <- data.frame (new.d, b1ba2)
result<-questionr::freq(temp.d$b1ba2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
| One |
478 |
8.2 |
63.3 |
| Two_or_more |
277 |
4.7 |
36.7 |
| NA |
5084 |
87.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
levels(b1bb) <- list(No="1",
Yes="2",
Dont_know="88")
b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1bb)
new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
temp.d <- data.frame (new.d, b1bb)
result<-questionr::freq(temp.d$b1bb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
| No |
1108 |
19.0 |
63.2 |
| Yes |
285 |
4.9 |
16.3 |
| Dont_know |
360 |
6.2 |
20.5 |
| NA |
4086 |
70.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
b1bc <- as.factor(d[,"b1bc"])
# Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
levels(b1bc) <- list(No="1",
Yes="2",
Dont_know="88")
b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1bc)
new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
temp.d <- data.frame (new.d, b1bc)
result<-questionr::freq(temp.d$b1bc,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
| No |
1417 |
24.3 |
82.0 |
| Yes |
141 |
2.4 |
8.2 |
| Dont_know |
169 |
2.9 |
9.8 |
| NA |
4112 |
70.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B1C: Any Son
- B1CNo: Any Son
- 1=I had no sons
- if not marked
- B1Ca: Any Son: Has this person had prostate cancer?
- B1Ca2: Any Son: If Yes, number with prostate cancer
- B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
- B1Cc: Any Son: Did he (or any) die of prostate cancer?
# B1BNo
b1cno <- as.factor(d[,"b1cno"])
levels(b1cno) <- list(No_brothers="1")
new.d <- data.frame(new.d, b1cno)
new.d <- apply_labels(new.d, b1cno = "Any Son")
temp.d <- data.frame (new.d, b1cno)
result<-questionr::freq(temp.d$b1cno,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
| No_brothers |
1082 |
18.5 |
100 |
| NA |
4757 |
81.5 |
NA |
| Total |
5839 |
100.0 |
100 |
#B1Ca
b1ca <- as.factor(d[,"b1ca"])
# Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
levels(b1ca) <- list(No="1",
Yes="2",
Dont_know="88")
b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ca)
new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
temp.d <- data.frame (new.d, b1ca)
result<-questionr::freq(temp.d$b1ca,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
| No |
4244 |
72.7 |
93.1 |
| Yes |
140 |
2.4 |
3.1 |
| Dont_know |
175 |
3.0 |
3.8 |
| NA |
1280 |
21.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Ca2
b1ca2 <- as.factor(d[,"b1ca2"])
# Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
levels(b1ca2) <- list(One="1",
Two_or_more="2")
b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
new.d <- data.frame(new.d, b1ca2)
new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
temp.d <- data.frame (new.d, b1ca2)
result<-questionr::freq(temp.d$b1ca2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
| One |
60 |
1.0 |
53.6 |
| Two_or_more |
52 |
0.9 |
46.4 |
| NA |
5727 |
98.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Cb
b1cb <- as.factor(d[,"b1cb"])
# Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
levels(b1cb) <- list(No="1",
Yes="2",
Dont_know="88")
b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1cb)
new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
temp.d <- data.frame (new.d, b1cb)
result<-questionr::freq(temp.d$b1cb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
| No |
780 |
13.4 |
84.1 |
| Yes |
27 |
0.5 |
2.9 |
| Dont_know |
120 |
2.1 |
12.9 |
| NA |
4912 |
84.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#B1Cc
b1cc <- as.factor(d[,"b1cc"])
# Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
levels(b1cc) <- list(No="1",
Yes="2",
Dont_know="88")
b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1cc)
new.d <- apply_labels(new.d, b1cc = "Any Son: die")
temp.d <- data.frame (new.d, b1cc)
result<-questionr::freq(temp.d$b1cc,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
| No |
817 |
14.0 |
89.2 |
| Yes |
6 |
0.1 |
0.7 |
| Dont_know |
93 |
1.6 |
10.2 |
| NA |
4923 |
84.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B1D: Maternal Grandfather
- B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
- B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
- b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
levels(b1da) <- list(No="1",
Yes="2",
Dont_know="88")
b1da <- ordered(b1da, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1da)
new.d <- apply_labels(new.d, b1da = "Father")
temp.d <- data.frame (new.d, b1da)
result<-questionr::freq(temp.d$b1da,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
| No |
2397 |
41.1 |
43.9 |
| Yes |
174 |
3.0 |
3.2 |
| Dont_know |
2886 |
49.4 |
52.9 |
| NA |
382 |
6.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
b1db <- as.factor(d[,"b1db"])
# Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
levels(b1db) <- list(No="1",
Yes="2",
Dont_know="88")
b1db <- ordered(b1db, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1db)
new.d <- apply_labels(new.d, b1db = "Father")
temp.d <- data.frame (new.d, b1db)
result<-questionr::freq(temp.d$b1db,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
| No |
433 |
7.4 |
36.6 |
| Yes |
21 |
0.4 |
1.8 |
| Dont_know |
730 |
12.5 |
61.7 |
| NA |
4655 |
79.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
b1dc <- as.factor(d[,"b1dc"])
# Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
levels(b1dc) <- list(No="1",
Yes="2",
Dont_know="88")
b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1dc)
new.d <- apply_labels(new.d, b1dc = "Father")
temp.d <- data.frame (new.d, b1dc)
result<-questionr::freq(temp.d$b1dc,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
| No |
456 |
7.8 |
38.0 |
| Yes |
73 |
1.3 |
6.1 |
| Dont_know |
670 |
11.5 |
55.9 |
| NA |
4640 |
79.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B1E: Paternal Grandfather
- B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
- B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
- B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
levels(b1ea) <- list(No="1",
Yes="2",
Dont_know="88")
b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ea)
new.d <- apply_labels(new.d, b1ea = "Father")
temp.d <- data.frame (new.d, b1ea)
result<-questionr::freq(temp.d$b1ea,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
| No |
2146 |
36.8 |
39.6 |
| Yes |
166 |
2.8 |
3.1 |
| Dont_know |
3103 |
53.1 |
57.3 |
| NA |
424 |
7.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
b1eb <- as.factor(d[,"b1eb"])
# Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
levels(b1eb) <- list(No="1",
Yes="2",
Dont_know="88")
b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1eb)
new.d <- apply_labels(new.d, b1eb = "Father")
temp.d <- data.frame (new.d, b1eb)
result<-questionr::freq(temp.d$b1eb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
| No |
372 |
6.4 |
32.3 |
| Yes |
24 |
0.4 |
2.1 |
| Dont_know |
754 |
12.9 |
65.6 |
| NA |
4689 |
80.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
b1ec <- as.factor(d[,"b1ec"])
# Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
levels(b1ec) <- list(No="1",
Yes="2",
Dont_know="88")
b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ec)
new.d <- apply_labels(new.d, b1ec = "Father")
temp.d <- data.frame (new.d, b1ec)
result<-questionr::freq(temp.d$b1ec,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
| No |
398 |
6.8 |
33.8 |
| Yes |
74 |
1.3 |
6.3 |
| Dont_know |
707 |
12.1 |
60.0 |
| NA |
4660 |
79.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B2: Family History (Other cancers)
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
Yes="2")
b2 <- ordered(b2, c("Yes","No"))
new.d <- data.frame(new.d, b2)
new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
temp.d <- data.frame (new.d, b2)
result<-questionr::freq(temp.d$b2, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B2")
B2
| Yes |
1455 |
24.9 |
38.7 |
| No |
2307 |
39.5 |
61.3 |
| NA |
2077 |
35.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B2A: Mother
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2A_1: 1=Breast
- B2A_2: 1=Ovarian
- B2A_3: 1=Colorectal
- B2A_4: 1=Lung
- B2A_5: 1=Other Cancer
b2a_1 <- as.factor(d[,"b2a_1"])
levels(b2a_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2a_1)
new.d <- apply_labels(new.d, b2a_1 = "Breast")
temp.d <- data.frame (new.d, b2a_1)
result<-questionr::freq(temp.d$b2a_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
551 |
9.4 |
100 |
| NA |
5288 |
90.6 |
NA |
| Total |
5839 |
100.0 |
100 |
b2a_2 <- as.factor(d[,"b2a_2"])
levels(b2a_2) <- list(Ovarian="1")
new.d <- data.frame(new.d, b2a_2)
new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
temp.d <- data.frame (new.d, b2a_2)
result<-questionr::freq(temp.d$b2a_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
| Ovarian |
226 |
3.9 |
100 |
| NA |
5613 |
96.1 |
NA |
| Total |
5839 |
100.0 |
100 |
b2a_3 <- as.factor(d[,"b2a_3"])
levels(b2a_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2a_3)
new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
temp.d <- data.frame (new.d, b2a_3)
result<-questionr::freq(temp.d$b2a_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
120 |
2.1 |
100 |
| NA |
5719 |
97.9 |
NA |
| Total |
5839 |
100.0 |
100 |
b2a_4 <- as.factor(d[,"b2a_4"])
levels(b2a_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2a_4)
new.d <- apply_labels(new.d, b2a_4 = "Lung")
temp.d <- data.frame (new.d, b2a_4)
result<-questionr::freq(temp.d$b2a_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
176 |
3 |
100 |
| NA |
5663 |
97 |
NA |
| Total |
5839 |
100 |
100 |
b2a_5 <- as.factor(d[,"b2a_5"])
levels(b2a_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2a_5)
new.d <- apply_labels(new.d, b2a_5 = "Lung")
temp.d <- data.frame (new.d, b2a_5)
result<-questionr::freq(temp.d$b2a_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
473 |
8.1 |
100 |
| NA |
5366 |
91.9 |
NA |
| Total |
5839 |
100.0 |
100 |
B2B: Father
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2B_1: 1=Breast
- B2B_3: 1=Colorectal
- B2B_4: 1=Lung
- B2B_5: 1=Other Cancer
b2b_1 <- as.factor(d[,"b2b_1"])
levels(b2b_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2b_1)
new.d <- apply_labels(new.d, b2b_1 = "Breast")
temp.d <- data.frame (new.d, b2b_1)
result<-questionr::freq(temp.d$b2b_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
39 |
0.7 |
100 |
| NA |
5800 |
99.3 |
NA |
| Total |
5839 |
100.0 |
100 |
b2b_3 <- as.factor(d[,"b2b_3"])
levels(b2b_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2b_3)
new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
temp.d <- data.frame (new.d, b2b_3)
result<-questionr::freq(temp.d$b2b_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
123 |
2.1 |
100 |
| NA |
5716 |
97.9 |
NA |
| Total |
5839 |
100.0 |
100 |
b2b_4 <- as.factor(d[,"b2b_4"])
levels(b2b_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2b_4)
new.d <- apply_labels(new.d, b2b_4 = "Lung")
temp.d <- data.frame (new.d, b2b_4)
result<-questionr::freq(temp.d$b2b_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
304 |
5.2 |
100 |
| NA |
5535 |
94.8 |
NA |
| Total |
5839 |
100.0 |
100 |
b2b_5 <- as.factor(d[,"b2b_5"])
levels(b2b_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2b_5)
new.d <- apply_labels(new.d, b2b_5 = "Lung")
temp.d <- data.frame (new.d, b2b_5)
result<-questionr::freq(temp.d$b2b_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
416 |
7.1 |
100 |
| NA |
5423 |
92.9 |
NA |
| Total |
5839 |
100.0 |
100 |
B2C: Any sister
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2C_1: 1=Breast
- B2C_2: 1=Ovarian
- B2C_3: 1=Colorectal
- B2C_4: 1=Lung
- B2C_5: 1=Other Cancer
b2c_1 <- as.factor(d[,"b2c_1"])
levels(b2c_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2c_1)
new.d <- apply_labels(new.d, b2c_1 = "Breast")
temp.d <- data.frame (new.d, b2c_1)
result<-questionr::freq(temp.d$b2c_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
593 |
10.2 |
100 |
| NA |
5246 |
89.8 |
NA |
| Total |
5839 |
100.0 |
100 |
b2c_2 <- as.factor(d[,"b2c_2"])
levels(b2c_2) <- list(Ovarian="1")
new.d <- data.frame(new.d, b2c_2)
new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
temp.d <- data.frame (new.d, b2c_2)
result<-questionr::freq(temp.d$b2c_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
| Ovarian |
171 |
2.9 |
100 |
| NA |
5668 |
97.1 |
NA |
| Total |
5839 |
100.0 |
100 |
b2c_3 <- as.factor(d[,"b2c_3"])
levels(b2c_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2c_3)
new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
temp.d <- data.frame (new.d, b2c_3)
result<-questionr::freq(temp.d$b2c_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
61 |
1 |
100 |
| NA |
5778 |
99 |
NA |
| Total |
5839 |
100 |
100 |
b2c_4 <- as.factor(d[,"b2c_4"])
levels(b2c_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2c_4)
new.d <- apply_labels(new.d, b2c_4 = "Lung")
temp.d <- data.frame (new.d, b2c_4)
result<-questionr::freq(temp.d$b2c_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
130 |
2.2 |
100 |
| NA |
5709 |
97.8 |
NA |
| Total |
5839 |
100.0 |
100 |
b2c_5 <- as.factor(d[,"b2c_5"])
levels(b2c_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2c_5)
new.d <- apply_labels(new.d, b2c_5 = "Lung")
temp.d <- data.frame (new.d, b2c_5)
result<-questionr::freq(temp.d$b2c_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
311 |
5.3 |
100 |
| NA |
5528 |
94.7 |
NA |
| Total |
5839 |
100.0 |
100 |
B2D: Any brother
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2D_1: 1=Breast
- B2D_3: 1=Colorectal
- B2D_4: 1=Lung
- B2D_5: 1=Other Cancer
b2d_1 <- as.factor(d[,"b2d_1"])
levels(b2d_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2d_1)
new.d <- apply_labels(new.d, b2d_1 = "Breast")
temp.d <- data.frame (new.d, b2d_1)
result<-questionr::freq(temp.d$b2d_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
39 |
0.7 |
100 |
| NA |
5800 |
99.3 |
NA |
| Total |
5839 |
100.0 |
100 |
b2d_3 <- as.factor(d[,"b2d_3"])
levels(b2d_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2d_3)
new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
temp.d <- data.frame (new.d, b2d_3)
result<-questionr::freq(temp.d$b2d_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
88 |
1.5 |
100 |
| NA |
5751 |
98.5 |
NA |
| Total |
5839 |
100.0 |
100 |
b2d_4 <- as.factor(d[,"b2d_4"])
levels(b2d_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2d_4)
new.d <- apply_labels(new.d, b2d_4 = "Lung")
temp.d <- data.frame (new.d, b2d_4)
result<-questionr::freq(temp.d$b2d_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
169 |
2.9 |
100 |
| NA |
5670 |
97.1 |
NA |
| Total |
5839 |
100.0 |
100 |
b2d_5 <- as.factor(d[,"b2d_5"])
levels(b2d_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2d_5)
new.d <- apply_labels(new.d, b2d_5 = "Lung")
temp.d <- data.frame (new.d, b2d_5)
result<-questionr::freq(temp.d$b2d_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
375 |
6.4 |
100 |
| NA |
5464 |
93.6 |
NA |
| Total |
5839 |
100.0 |
100 |
B2E: Any daughter
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2E_1: 1=Breast
- B2E_2: 1=Ovarian
- B2E_3: 1=Colorectal
- B2E_4: 1=Lung
- B2E_5: 1=Other Cancer
b2e_1 <- as.factor(d[,"b2e_1"])
levels(b2e_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2e_1)
new.d <- apply_labels(new.d, b2e_1 = "Breast")
temp.d <- data.frame (new.d, b2e_1)
result<-questionr::freq(temp.d$b2e_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
55 |
0.9 |
100 |
| NA |
5784 |
99.1 |
NA |
| Total |
5839 |
100.0 |
100 |
b2e_2 <- as.factor(d[,"b2e_2"])
levels(b2e_2) <- list(Ovarian="1")
new.d <- data.frame(new.d, b2e_2)
new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
temp.d <- data.frame (new.d, b2e_2)
result<-questionr::freq(temp.d$b2e_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
| Ovarian |
89 |
1.5 |
100 |
| NA |
5750 |
98.5 |
NA |
| Total |
5839 |
100.0 |
100 |
b2e_3 <- as.factor(d[,"b2e_3"])
levels(b2e_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2e_3)
new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
temp.d <- data.frame (new.d, b2e_3)
result<-questionr::freq(temp.d$b2e_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
5 |
0.1 |
100 |
| NA |
5834 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
b2e_4 <- as.factor(d[,"b2e_4"])
levels(b2e_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2e_4)
new.d <- apply_labels(new.d, b2e_4 = "Lung")
temp.d <- data.frame (new.d, b2e_4)
result<-questionr::freq(temp.d$b2e_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
9 |
0.2 |
100 |
| NA |
5830 |
99.8 |
NA |
| Total |
5839 |
100.0 |
100 |
b2e_5 <- as.factor(d[,"b2e_5"])
levels(b2e_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2e_5)
new.d <- apply_labels(new.d, b2e_5 = "Lung")
temp.d <- data.frame (new.d, b2e_5)
result<-questionr::freq(temp.d$b2e_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
29 |
0.5 |
100 |
| NA |
5810 |
99.5 |
NA |
| Total |
5839 |
100.0 |
100 |
B2F: Any son
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2F_1: 1=Breast
- B2F_3: 1=Colorectal
- B2F_4: 1=Lung
- B2F_5: 1=Other Cancer
b2f_1 <- as.factor(d[,"b2f_1"])
levels(b2f_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2f_1)
new.d <- apply_labels(new.d, b2f_1 = "Breast")
temp.d <- data.frame (new.d, b2f_1)
result<-questionr::freq(temp.d$b2f_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
16 |
0.3 |
100 |
| NA |
5823 |
99.7 |
NA |
| Total |
5839 |
100.0 |
100 |
b2f_3 <- as.factor(d[,"b2f_3"])
levels(b2f_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2f_3)
new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
temp.d <- data.frame (new.d, b2f_3)
result<-questionr::freq(temp.d$b2f_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
12 |
0.2 |
100 |
| NA |
5827 |
99.8 |
NA |
| Total |
5839 |
100.0 |
100 |
b2f_4 <- as.factor(d[,"b2f_4"])
levels(b2f_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2f_4)
new.d <- apply_labels(new.d, b2f_4 = "Lung")
temp.d <- data.frame (new.d, b2f_4)
result<-questionr::freq(temp.d$b2f_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
8 |
0.1 |
100 |
| NA |
5831 |
99.9 |
NA |
| Total |
5839 |
100.0 |
100 |
b2f_5 <- as.factor(d[,"b2f_5"])
levels(b2f_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2f_5)
new.d <- apply_labels(new.d, b2f_5 = "Lung")
temp.d <- data.frame (new.d, b2f_5)
result<-questionr::freq(temp.d$b2f_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
28 |
0.5 |
100 |
| NA |
5811 |
99.5 |
NA |
| Total |
5839 |
100.0 |
100 |
B3: Current health
- B3. In general, how would you rate your current health?
- 1=Excellent
- 2=Very Good
- 3=Good
- 4=Fair
- 5=Poor
b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
levels(b3) <- list(Excellent="1",
Very_Good="2",
Good="3",
Fair="4",
Poor="5")
b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))
new.d <- data.frame(new.d, b3)
new.d <- apply_labels(new.d, b3 = "Current Health")
temp.d <- data.frame (new.d, b3)
result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
kable(result, format = "simple", align = 'l')
| Excellent |
387 |
6.6 |
6.9 |
6.6 |
6.9 |
| Very_Good |
1566 |
26.8 |
27.9 |
33.4 |
34.8 |
| Good |
2378 |
40.7 |
42.3 |
74.2 |
77.1 |
| Fair |
1149 |
19.7 |
20.5 |
93.9 |
97.5 |
| Poor |
138 |
2.4 |
2.5 |
96.2 |
100.0 |
| NA |
221 |
3.8 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
B4: Comorbidities
- B4. Has the doctor ever told you that you have/had…
- Heart Attack
- Heart Failure or CHF
- Stroke
- Hypertension
- Peripheral arterial disease
- High Cholesterol
- Asthma, COPD
- Stomach ulcers
- Crohn’s Disease
- Diabetes
- Kidney Problems
- Cirrhosis, liver damage
- Arthritis
- Dementia
- Depression
- AIDS
- Other Cancer
# Heart Attack
b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
levels(b4aa) <- list(No="1",
Yes="2")
b4aa <- ordered(b4aa, c("No", "Yes"))
new.d <- data.frame(new.d, b4aa)
new.d <- apply_labels(new.d, b4aa = "Heart Attack")
temp.d <- data.frame (new.d, b4aa)
result<-questionr::freq(temp.d$b4aa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
| No |
5145 |
88.1 |
93.1 |
| Yes |
379 |
6.5 |
6.9 |
| NA |
315 |
5.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4ab <- as.factor(d[,"b4ab"])
new.d <- data.frame(new.d, b4ab)
new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
temp.d <- data.frame (new.d, b4ab)
result<-questionr::freq(temp.d$b4ab, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
| 0 |
77 |
1.3 |
17.9 |
| 1 |
2 |
0.0 |
0.5 |
| 14 |
1 |
0.0 |
0.2 |
| 17 |
3 |
0.1 |
0.7 |
| 19 |
1 |
0.0 |
0.2 |
| 20 |
1 |
0.0 |
0.2 |
| 23 |
2 |
0.0 |
0.5 |
| 24 |
2 |
0.0 |
0.5 |
| 25 |
2 |
0.0 |
0.5 |
| 26 |
1 |
0.0 |
0.2 |
| 27 |
2 |
0.0 |
0.5 |
| 29 |
4 |
0.1 |
0.9 |
| 31 |
2 |
0.0 |
0.5 |
| 32 |
2 |
0.0 |
0.5 |
| 33 |
1 |
0.0 |
0.2 |
| 34 |
1 |
0.0 |
0.2 |
| 35 |
4 |
0.1 |
0.9 |
| 36 |
1 |
0.0 |
0.2 |
| 38 |
3 |
0.1 |
0.7 |
| 40 |
7 |
0.1 |
1.6 |
| 41 |
2 |
0.0 |
0.5 |
| 42 |
1 |
0.0 |
0.2 |
| 44 |
3 |
0.1 |
0.7 |
| 45 |
8 |
0.1 |
1.9 |
| 46 |
6 |
0.1 |
1.4 |
| 47 |
3 |
0.1 |
0.7 |
| 48 |
8 |
0.1 |
1.9 |
| 49 |
6 |
0.1 |
1.4 |
| 5 |
1 |
0.0 |
0.2 |
| 50 |
26 |
0.4 |
6.1 |
| 51 |
9 |
0.2 |
2.1 |
| 52 |
14 |
0.2 |
3.3 |
| 53 |
8 |
0.1 |
1.9 |
| 54 |
9 |
0.2 |
2.1 |
| 55 |
19 |
0.3 |
4.4 |
| 56 |
5 |
0.1 |
1.2 |
| 57 |
13 |
0.2 |
3.0 |
| 58 |
20 |
0.3 |
4.7 |
| 59 |
12 |
0.2 |
2.8 |
| 60 |
21 |
0.4 |
4.9 |
| 61 |
10 |
0.2 |
2.3 |
| 62 |
6 |
0.1 |
1.4 |
| 63 |
9 |
0.2 |
2.1 |
| 64 |
15 |
0.3 |
3.5 |
| 65 |
11 |
0.2 |
2.6 |
| 66 |
5 |
0.1 |
1.2 |
| 67 |
9 |
0.2 |
2.1 |
| 68 |
4 |
0.1 |
0.9 |
| 69 |
11 |
0.2 |
2.6 |
| 70 |
8 |
0.1 |
1.9 |
| 71 |
7 |
0.1 |
1.6 |
| 72 |
4 |
0.1 |
0.9 |
| 73 |
4 |
0.1 |
0.9 |
| 74 |
7 |
0.1 |
1.6 |
| 75 |
1 |
0.0 |
0.2 |
| 76 |
3 |
0.1 |
0.7 |
| 79 |
1 |
0.0 |
0.2 |
| 93 |
1 |
0.0 |
0.2 |
| NA |
5410 |
92.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Heart Failure or CHF
b4ba <- as.factor(d[,"b4ba"])
# Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
levels(b4ba) <- list(No="1",
Yes="2")
b4ba <- ordered(b4ba, c("No", "Yes"))
new.d <- data.frame(new.d, b4ba)
new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
temp.d <- data.frame (new.d, b4ba)
result<-questionr::freq(temp.d$b4ba, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
| No |
5134 |
87.9 |
93.9 |
| Yes |
333 |
5.7 |
6.1 |
| NA |
372 |
6.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4bb <- as.factor(d[,"b4bb"])
new.d <- data.frame(new.d, b4bb)
new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
temp.d <- data.frame (new.d, b4bb)
result<-questionr::freq(temp.d$b4bb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
| 1 |
1 |
0.0 |
0.3 |
| 12 |
1 |
0.0 |
0.3 |
| 13 |
1 |
0.0 |
0.3 |
| 17 |
1 |
0.0 |
0.3 |
| 18 |
1 |
0.0 |
0.3 |
| 20 |
2 |
0.0 |
0.7 |
| 24 |
1 |
0.0 |
0.3 |
| 25 |
1 |
0.0 |
0.3 |
| 27 |
1 |
0.0 |
0.3 |
| 28 |
1 |
0.0 |
0.3 |
| 29 |
1 |
0.0 |
0.3 |
| 30 |
2 |
0.0 |
0.7 |
| 31 |
1 |
0.0 |
0.3 |
| 32 |
1 |
0.0 |
0.3 |
| 34 |
1 |
0.0 |
0.3 |
| 35 |
2 |
0.0 |
0.7 |
| 36 |
1 |
0.0 |
0.3 |
| 39 |
2 |
0.0 |
0.7 |
| 4 |
1 |
0.0 |
0.3 |
| 40 |
5 |
0.1 |
1.7 |
| 42 |
1 |
0.0 |
0.3 |
| 43 |
3 |
0.1 |
1.0 |
| 45 |
2 |
0.0 |
0.7 |
| 46 |
3 |
0.1 |
1.0 |
| 47 |
4 |
0.1 |
1.3 |
| 48 |
4 |
0.1 |
1.3 |
| 49 |
7 |
0.1 |
2.3 |
| 5 |
2 |
0.0 |
0.7 |
| 50 |
15 |
0.3 |
5.0 |
| 51 |
5 |
0.1 |
1.7 |
| 52 |
11 |
0.2 |
3.7 |
| 53 |
5 |
0.1 |
1.7 |
| 54 |
6 |
0.1 |
2.0 |
| 55 |
10 |
0.2 |
3.3 |
| 56 |
12 |
0.2 |
4.0 |
| 57 |
6 |
0.1 |
2.0 |
| 58 |
9 |
0.2 |
3.0 |
| 59 |
12 |
0.2 |
4.0 |
| 60 |
20 |
0.3 |
6.7 |
| 61 |
7 |
0.1 |
2.3 |
| 62 |
15 |
0.3 |
5.0 |
| 63 |
12 |
0.2 |
4.0 |
| 64 |
9 |
0.2 |
3.0 |
| 65 |
13 |
0.2 |
4.3 |
| 66 |
7 |
0.1 |
2.3 |
| 67 |
7 |
0.1 |
2.3 |
| 68 |
11 |
0.2 |
3.7 |
| 69 |
7 |
0.1 |
2.3 |
| 7 |
1 |
0.0 |
0.3 |
| 70 |
10 |
0.2 |
3.3 |
| 71 |
7 |
0.1 |
2.3 |
| 72 |
7 |
0.1 |
2.3 |
| 73 |
5 |
0.1 |
1.7 |
| 74 |
3 |
0.1 |
1.0 |
| 75 |
4 |
0.1 |
1.3 |
| 76 |
1 |
0.0 |
0.3 |
| 77 |
1 |
0.0 |
0.3 |
| 78 |
2 |
0.0 |
0.7 |
| 80 |
2 |
0.0 |
0.7 |
| 82 |
1 |
0.0 |
0.3 |
| 83 |
1 |
0.0 |
0.3 |
| 95 |
1 |
0.0 |
0.3 |
| 97 |
1 |
0.0 |
0.3 |
| NA |
5539 |
94.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Stroke
b4ca <- as.factor(d[,"b4ca"])
# Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
levels(b4ca) <- list(No="1",
Yes="2")
b4ca <- ordered(b4ca, c("No", "Yes"))
new.d <- data.frame(new.d, b4ca)
new.d <- apply_labels(new.d, b4ca = "Stroke")
temp.d <- data.frame (new.d, b4ca)
result<-questionr::freq(temp.d$b4ca,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
| No |
4989 |
85.4 |
91.1 |
| Yes |
486 |
8.3 |
8.9 |
| NA |
364 |
6.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4cb <- as.factor(d[,"b4cb"])
new.d <- data.frame(new.d, b4cb)
new.d <- apply_labels(new.d, b4cb = "Stroke age")
temp.d <- data.frame (new.d, b4cb)
result<-questionr::freq(temp.d$b4cb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
| 0 |
104 |
1.8 |
19.0 |
| 12 |
1 |
0.0 |
0.2 |
| 15 |
1 |
0.0 |
0.2 |
| 16 |
1 |
0.0 |
0.2 |
| 19 |
1 |
0.0 |
0.2 |
| 20 |
2 |
0.0 |
0.4 |
| 23 |
1 |
0.0 |
0.2 |
| 25 |
4 |
0.1 |
0.7 |
| 27 |
3 |
0.1 |
0.5 |
| 30 |
3 |
0.1 |
0.5 |
| 31 |
2 |
0.0 |
0.4 |
| 34 |
2 |
0.0 |
0.4 |
| 35 |
3 |
0.1 |
0.5 |
| 38 |
1 |
0.0 |
0.2 |
| 40 |
6 |
0.1 |
1.1 |
| 42 |
5 |
0.1 |
0.9 |
| 43 |
5 |
0.1 |
0.9 |
| 45 |
11 |
0.2 |
2.0 |
| 46 |
6 |
0.1 |
1.1 |
| 47 |
4 |
0.1 |
0.7 |
| 48 |
7 |
0.1 |
1.3 |
| 49 |
6 |
0.1 |
1.1 |
| 5 |
3 |
0.1 |
0.5 |
| 50 |
19 |
0.3 |
3.5 |
| 51 |
8 |
0.1 |
1.5 |
| 52 |
14 |
0.2 |
2.6 |
| 53 |
4 |
0.1 |
0.7 |
| 54 |
14 |
0.2 |
2.6 |
| 55 |
21 |
0.4 |
3.8 |
| 56 |
15 |
0.3 |
2.7 |
| 57 |
16 |
0.3 |
2.9 |
| 58 |
10 |
0.2 |
1.8 |
| 59 |
18 |
0.3 |
3.3 |
| 60 |
27 |
0.5 |
4.9 |
| 61 |
17 |
0.3 |
3.1 |
| 62 |
15 |
0.3 |
2.7 |
| 63 |
23 |
0.4 |
4.2 |
| 64 |
16 |
0.3 |
2.9 |
| 65 |
23 |
0.4 |
4.2 |
| 66 |
13 |
0.2 |
2.4 |
| 67 |
10 |
0.2 |
1.8 |
| 68 |
23 |
0.4 |
4.2 |
| 69 |
15 |
0.3 |
2.7 |
| 70 |
9 |
0.2 |
1.6 |
| 71 |
4 |
0.1 |
0.7 |
| 72 |
5 |
0.1 |
0.9 |
| 73 |
3 |
0.1 |
0.5 |
| 74 |
3 |
0.1 |
0.5 |
| 75 |
2 |
0.0 |
0.4 |
| 76 |
3 |
0.1 |
0.5 |
| 77 |
3 |
0.1 |
0.5 |
| 78 |
3 |
0.1 |
0.5 |
| 79 |
4 |
0.1 |
0.7 |
| 8 |
1 |
0.0 |
0.2 |
| 80 |
1 |
0.0 |
0.2 |
| 86 |
1 |
0.0 |
0.2 |
| 88 |
1 |
0.0 |
0.2 |
| 9 |
1 |
0.0 |
0.2 |
| NA |
5292 |
90.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Hypertension
b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
levels(b4da) <- list(No="1",
Yes="2")
b4da <- ordered(b4da, c("No", "Yes"))
new.d <- data.frame(new.d, b4da)
new.d <- apply_labels(new.d, b4da = "Hypertension")
temp.d <- data.frame (new.d, b4da)
result<-questionr::freq(temp.d$b4da, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
| No |
1500 |
25.7 |
26.8 |
| Yes |
4089 |
70.0 |
73.2 |
| NA |
250 |
4.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4db <- as.factor(d[,"b4db"])
new.d <- data.frame(new.d, b4db)
new.d <- apply_labels(new.d, b4db = "Hypertension age")
temp.d <- data.frame (new.d, b4db)
result<-questionr::freq(temp.d$b4db, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
| .35 |
1 |
0.0 |
0.0 |
| 1 |
2 |
0.0 |
0.1 |
| 10 |
1 |
0.0 |
0.0 |
| 12 |
5 |
0.1 |
0.1 |
| 13 |
2 |
0.0 |
0.1 |
| 14 |
2 |
0.0 |
0.1 |
| 15 |
5 |
0.1 |
0.1 |
| 16 |
11 |
0.2 |
0.3 |
| 17 |
3 |
0.1 |
0.1 |
| 18 |
12 |
0.2 |
0.3 |
| 19 |
6 |
0.1 |
0.2 |
| 2 |
3 |
0.1 |
0.1 |
| 20 |
19 |
0.3 |
0.5 |
| 21 |
6 |
0.1 |
0.2 |
| 22 |
12 |
0.2 |
0.3 |
| 23 |
11 |
0.2 |
0.3 |
| 24 |
13 |
0.2 |
0.4 |
| 25 |
32 |
0.5 |
0.9 |
| 26 |
6 |
0.1 |
0.2 |
| 27 |
12 |
0.2 |
0.3 |
| 28 |
18 |
0.3 |
0.5 |
| 29 |
14 |
0.2 |
0.4 |
| 30 |
109 |
1.9 |
3.0 |
| 31 |
19 |
0.3 |
0.5 |
| 32 |
26 |
0.4 |
0.7 |
| 33 |
16 |
0.3 |
0.4 |
| 34 |
15 |
0.3 |
0.4 |
| 35 |
144 |
2.5 |
4.0 |
| 36 |
27 |
0.5 |
0.8 |
| 37 |
20 |
0.3 |
0.6 |
| 38 |
42 |
0.7 |
1.2 |
| 39 |
31 |
0.5 |
0.9 |
| 4 |
3 |
0.1 |
0.1 |
| 40 |
270 |
4.6 |
7.5 |
| 41 |
26 |
0.4 |
0.7 |
| 42 |
60 |
1.0 |
1.7 |
| 43 |
28 |
0.5 |
0.8 |
| 44 |
31 |
0.5 |
0.9 |
| 45 |
250 |
4.3 |
7.0 |
| 46 |
41 |
0.7 |
1.1 |
| 47 |
45 |
0.8 |
1.3 |
| 48 |
60 |
1.0 |
1.7 |
| 49 |
46 |
0.8 |
1.3 |
| 5 |
5 |
0.1 |
0.1 |
| 50 |
409 |
7.0 |
11.4 |
| 51 |
45 |
0.8 |
1.3 |
| 52 |
78 |
1.3 |
2.2 |
| 53 |
37 |
0.6 |
1.0 |
| 54 |
63 |
1.1 |
1.8 |
| 55 |
271 |
4.6 |
7.5 |
| 56 |
76 |
1.3 |
2.1 |
| 57 |
66 |
1.1 |
1.8 |
| 58 |
99 |
1.7 |
2.8 |
| 59 |
54 |
0.9 |
1.5 |
| 6 |
1 |
0.0 |
0.0 |
| 60 |
272 |
4.7 |
7.6 |
| 61 |
55 |
0.9 |
1.5 |
| 62 |
87 |
1.5 |
2.4 |
| 63 |
47 |
0.8 |
1.3 |
| 64 |
50 |
0.9 |
1.4 |
| 65 |
92 |
1.6 |
2.6 |
| 66 |
23 |
0.4 |
0.6 |
| 67 |
38 |
0.7 |
1.1 |
| 68 |
45 |
0.8 |
1.3 |
| 69 |
26 |
0.4 |
0.7 |
| 7 |
2 |
0.0 |
0.1 |
| 70 |
47 |
0.8 |
1.3 |
| 71 |
17 |
0.3 |
0.5 |
| 72 |
18 |
0.3 |
0.5 |
| 73 |
10 |
0.2 |
0.3 |
| 74 |
8 |
0.1 |
0.2 |
| 75 |
5 |
0.1 |
0.1 |
| 76 |
4 |
0.1 |
0.1 |
| 77 |
1 |
0.0 |
0.0 |
| 78 |
1 |
0.0 |
0.0 |
| 79 |
4 |
0.1 |
0.1 |
| 8 |
4 |
0.1 |
0.1 |
| 80 |
3 |
0.1 |
0.1 |
| 81 |
1 |
0.0 |
0.0 |
| 89 |
3 |
0.1 |
0.1 |
| 9 |
4 |
0.1 |
0.1 |
| 91 |
1 |
0.0 |
0.0 |
| 92 |
1 |
0.0 |
0.0 |
| 93 |
1 |
0.0 |
0.0 |
| 94 |
3 |
0.1 |
0.1 |
| 96 |
1 |
0.0 |
0.0 |
| 97 |
1 |
0.0 |
0.0 |
| 98 |
1 |
0.0 |
0.0 |
| 99 |
5 |
0.1 |
0.1 |
| NA |
2249 |
38.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Peripheral arterial disease
b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"
levels(b4ea) <- list(No="1",
Yes="2")
b4ea <- ordered(b4ea, c("No", "Yes"))
new.d <- data.frame(new.d, b4ea)
new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
temp.d <- data.frame (new.d, b4ea)
result<-questionr::freq(temp.d$b4ea,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
| No |
5048 |
86.5 |
94 |
| Yes |
324 |
5.5 |
6 |
| NA |
467 |
8.0 |
NA |
| Total |
5839 |
100.0 |
100 |
b4eb <- as.factor(d[,"b4eb"])
new.d <- data.frame(new.d, b4eb)
new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
temp.d <- data.frame (new.d, b4eb)
result<-questionr::freq(temp.d$b4eb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
| * |
1 |
0.0 |
0.3 |
| ** |
1 |
0.0 |
0.3 |
| 0 |
27 |
0.5 |
6.8 |
| 1 |
1 |
0.0 |
0.3 |
| 16 |
1 |
0.0 |
0.3 |
| 17 |
1 |
0.0 |
0.3 |
| 19 |
1 |
0.0 |
0.3 |
| 20 |
1 |
0.0 |
0.3 |
| 22 |
1 |
0.0 |
0.3 |
| 25 |
3 |
0.1 |
0.8 |
| 26 |
1 |
0.0 |
0.3 |
| 30 |
2 |
0.0 |
0.5 |
| 31 |
1 |
0.0 |
0.3 |
| 33 |
1 |
0.0 |
0.3 |
| 34 |
3 |
0.1 |
0.8 |
| 35 |
5 |
0.1 |
1.3 |
| 36 |
1 |
0.0 |
0.3 |
| 37 |
1 |
0.0 |
0.3 |
| 40 |
16 |
0.3 |
4.1 |
| 41 |
1 |
0.0 |
0.3 |
| 42 |
2 |
0.0 |
0.5 |
| 43 |
1 |
0.0 |
0.3 |
| 44 |
2 |
0.0 |
0.5 |
| 45 |
8 |
0.1 |
2.0 |
| 46 |
2 |
0.0 |
0.5 |
| 47 |
2 |
0.0 |
0.5 |
| 48 |
8 |
0.1 |
2.0 |
| 49 |
4 |
0.1 |
1.0 |
| 5 |
1 |
0.0 |
0.3 |
| 50 |
36 |
0.6 |
9.1 |
| 51 |
7 |
0.1 |
1.8 |
| 52 |
4 |
0.1 |
1.0 |
| 53 |
4 |
0.1 |
1.0 |
| 54 |
8 |
0.1 |
2.0 |
| 55 |
23 |
0.4 |
5.8 |
| 56 |
8 |
0.1 |
2.0 |
| 57 |
11 |
0.2 |
2.8 |
| 58 |
16 |
0.3 |
4.1 |
| 59 |
7 |
0.1 |
1.8 |
| 60 |
32 |
0.5 |
8.1 |
| 61 |
6 |
0.1 |
1.5 |
| 62 |
14 |
0.2 |
3.5 |
| 63 |
8 |
0.1 |
2.0 |
| 64 |
13 |
0.2 |
3.3 |
| 65 |
22 |
0.4 |
5.6 |
| 66 |
6 |
0.1 |
1.5 |
| 67 |
7 |
0.1 |
1.8 |
| 68 |
12 |
0.2 |
3.0 |
| 69 |
8 |
0.1 |
2.0 |
| 70 |
17 |
0.3 |
4.3 |
| 71 |
2 |
0.0 |
0.5 |
| 72 |
4 |
0.1 |
1.0 |
| 73 |
5 |
0.1 |
1.3 |
| 74 |
3 |
0.1 |
0.8 |
| 76 |
3 |
0.1 |
0.8 |
| 77 |
1 |
0.0 |
0.3 |
| 78 |
2 |
0.0 |
0.5 |
| 82 |
2 |
0.0 |
0.5 |
| 84 |
1 |
0.0 |
0.3 |
| 94 |
1 |
0.0 |
0.3 |
| 97 |
1 |
0.0 |
0.3 |
| 98 |
1 |
0.0 |
0.3 |
| NA |
5444 |
93.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# High Cholesterol
b4fa <- as.factor(d[,"b4fa"])
# Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
levels(b4fa) <- list(No="1",
Yes="2")
b4fa <- ordered(b4fa, c("No", "Yes"))
new.d <- data.frame(new.d, b4fa)
new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
temp.d <- data.frame (new.d, b4fa)
result<-questionr::freq(temp.d$b4fa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
| No |
2499 |
42.8 |
45.4 |
| Yes |
3004 |
51.4 |
54.6 |
| NA |
336 |
5.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4fb <- as.factor(d[,"b4fb"])
new.d <- data.frame(new.d, b4fb)
new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
temp.d <- data.frame (new.d, b4fb)
result<-questionr::freq(temp.d$b4fb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
| 1 |
3 |
0.1 |
0.1 |
| 10 |
5 |
0.1 |
0.2 |
| 12 |
2 |
0.0 |
0.1 |
| 13 |
1 |
0.0 |
0.0 |
| 14 |
1 |
0.0 |
0.0 |
| 15 |
3 |
0.1 |
0.1 |
| 16 |
2 |
0.0 |
0.1 |
| 17 |
1 |
0.0 |
0.0 |
| 18 |
1 |
0.0 |
0.0 |
| 19 |
4 |
0.1 |
0.2 |
| 2 |
2 |
0.0 |
0.1 |
| 20 |
5 |
0.1 |
0.2 |
| 21 |
4 |
0.1 |
0.2 |
| 22 |
3 |
0.1 |
0.1 |
| 23 |
1 |
0.0 |
0.0 |
| 24 |
8 |
0.1 |
0.3 |
| 25 |
7 |
0.1 |
0.3 |
| 26 |
4 |
0.1 |
0.2 |
| 27 |
4 |
0.1 |
0.2 |
| 28 |
8 |
0.1 |
0.3 |
| 29 |
5 |
0.1 |
0.2 |
| 30 |
34 |
0.6 |
1.4 |
| 31 |
4 |
0.1 |
0.2 |
| 32 |
12 |
0.2 |
0.5 |
| 33 |
6 |
0.1 |
0.2 |
| 34 |
12 |
0.2 |
0.5 |
| 35 |
53 |
0.9 |
2.1 |
| 36 |
20 |
0.3 |
0.8 |
| 37 |
6 |
0.1 |
0.2 |
| 38 |
23 |
0.4 |
0.9 |
| 39 |
12 |
0.2 |
0.5 |
| 4 |
1 |
0.0 |
0.0 |
| 40 |
134 |
2.3 |
5.4 |
| 41 |
17 |
0.3 |
0.7 |
| 42 |
27 |
0.5 |
1.1 |
| 43 |
15 |
0.3 |
0.6 |
| 44 |
16 |
0.3 |
0.6 |
| 45 |
148 |
2.5 |
5.9 |
| 46 |
30 |
0.5 |
1.2 |
| 47 |
19 |
0.3 |
0.8 |
| 48 |
48 |
0.8 |
1.9 |
| 49 |
28 |
0.5 |
1.1 |
| 5 |
6 |
0.1 |
0.2 |
| 50 |
295 |
5.1 |
11.9 |
| 51 |
35 |
0.6 |
1.4 |
| 52 |
54 |
0.9 |
2.2 |
| 53 |
31 |
0.5 |
1.2 |
| 54 |
47 |
0.8 |
1.9 |
| 55 |
227 |
3.9 |
9.1 |
| 56 |
55 |
0.9 |
2.2 |
| 57 |
66 |
1.1 |
2.7 |
| 58 |
79 |
1.4 |
3.2 |
| 59 |
49 |
0.8 |
2.0 |
| 6 |
2 |
0.0 |
0.1 |
| 60 |
221 |
3.8 |
8.9 |
| 61 |
35 |
0.6 |
1.4 |
| 62 |
85 |
1.5 |
3.4 |
| 63 |
44 |
0.8 |
1.8 |
| 64 |
44 |
0.8 |
1.8 |
| 65 |
93 |
1.6 |
3.7 |
| 66 |
25 |
0.4 |
1.0 |
| 67 |
30 |
0.5 |
1.2 |
| 68 |
43 |
0.7 |
1.7 |
| 69 |
30 |
0.5 |
1.2 |
| 7 |
2 |
0.0 |
0.1 |
| 70 |
60 |
1.0 |
2.4 |
| 71 |
13 |
0.2 |
0.5 |
| 72 |
17 |
0.3 |
0.7 |
| 73 |
12 |
0.2 |
0.5 |
| 74 |
12 |
0.2 |
0.5 |
| 75 |
14 |
0.2 |
0.6 |
| 76 |
6 |
0.1 |
0.2 |
| 77 |
3 |
0.1 |
0.1 |
| 8 |
1 |
0.0 |
0.0 |
| 80 |
1 |
0.0 |
0.0 |
| 86 |
1 |
0.0 |
0.0 |
| 87 |
1 |
0.0 |
0.0 |
| 9 |
1 |
0.0 |
0.0 |
| 90 |
1 |
0.0 |
0.0 |
| 91 |
2 |
0.0 |
0.1 |
| 92 |
1 |
0.0 |
0.0 |
| 94 |
1 |
0.0 |
0.0 |
| 95 |
1 |
0.0 |
0.0 |
| 96 |
1 |
0.0 |
0.0 |
| 97 |
1 |
0.0 |
0.0 |
| 99 |
2 |
0.0 |
0.1 |
| NA |
3350 |
57.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Asthma, COPD
b4ga <- as.factor(d[,"b4ga"])
# Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
levels(b4ga) <- list(No="1",
Yes="2")
b4ga <- ordered(b4ga, c("No", "Yes"))
new.d <- data.frame(new.d, b4ga)
new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
temp.d <- data.frame (new.d, b4ga)
result<-questionr::freq(temp.d$b4ga, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
| No |
4826 |
82.7 |
85 |
| Yes |
853 |
14.6 |
15 |
| NA |
160 |
2.7 |
NA |
| Total |
5839 |
100.0 |
100 |
b4gb <- as.factor(d[,"b4gb"])
new.d <- data.frame(new.d, b4gb)
new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
temp.d <- data.frame (new.d, b4gb)
result<-questionr::freq(temp.d$b4gb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
| 04 |
1 |
0.0 |
0.1 |
| 06 |
2 |
0.0 |
0.3 |
| 1 |
16 |
0.3 |
2.1 |
| 10 |
37 |
0.6 |
4.8 |
| 11 |
6 |
0.1 |
0.8 |
| 12 |
17 |
0.3 |
2.2 |
| 13 |
5 |
0.1 |
0.7 |
| 14 |
5 |
0.1 |
0.7 |
| 15 |
6 |
0.1 |
0.8 |
| 16 |
6 |
0.1 |
0.8 |
| 17 |
2 |
0.0 |
0.3 |
| 18 |
14 |
0.2 |
1.8 |
| 19 |
3 |
0.1 |
0.4 |
| 2 |
10 |
0.2 |
1.3 |
| 20 |
8 |
0.1 |
1.0 |
| 21 |
2 |
0.0 |
0.3 |
| 22 |
2 |
0.0 |
0.3 |
| 23 |
1 |
0.0 |
0.1 |
| 24 |
3 |
0.1 |
0.4 |
| 25 |
6 |
0.1 |
0.8 |
| 26 |
2 |
0.0 |
0.3 |
| 27 |
3 |
0.1 |
0.4 |
| 28 |
3 |
0.1 |
0.4 |
| 29 |
1 |
0.0 |
0.1 |
| 3 |
5 |
0.1 |
0.7 |
| 30 |
13 |
0.2 |
1.7 |
| 31 |
2 |
0.0 |
0.3 |
| 32 |
3 |
0.1 |
0.4 |
| 33 |
2 |
0.0 |
0.3 |
| 35 |
9 |
0.2 |
1.2 |
| 36 |
2 |
0.0 |
0.3 |
| 37 |
1 |
0.0 |
0.1 |
| 38 |
6 |
0.1 |
0.8 |
| 39 |
1 |
0.0 |
0.1 |
| 4 |
12 |
0.2 |
1.6 |
| 40 |
19 |
0.3 |
2.5 |
| 42 |
6 |
0.1 |
0.8 |
| 43 |
3 |
0.1 |
0.4 |
| 44 |
2 |
0.0 |
0.3 |
| 45 |
14 |
0.2 |
1.8 |
| 46 |
4 |
0.1 |
0.5 |
| 47 |
4 |
0.1 |
0.5 |
| 48 |
6 |
0.1 |
0.8 |
| 49 |
5 |
0.1 |
0.7 |
| 5 |
47 |
0.8 |
6.1 |
| 50 |
41 |
0.7 |
5.4 |
| 51 |
7 |
0.1 |
0.9 |
| 52 |
7 |
0.1 |
0.9 |
| 53 |
5 |
0.1 |
0.7 |
| 54 |
5 |
0.1 |
0.7 |
| 55 |
24 |
0.4 |
3.1 |
| 56 |
15 |
0.3 |
2.0 |
| 57 |
15 |
0.3 |
2.0 |
| 58 |
12 |
0.2 |
1.6 |
| 59 |
18 |
0.3 |
2.3 |
| 6 |
19 |
0.3 |
2.5 |
| 60 |
50 |
0.9 |
6.5 |
| 61 |
7 |
0.1 |
0.9 |
| 62 |
19 |
0.3 |
2.5 |
| 63 |
19 |
0.3 |
2.5 |
| 64 |
11 |
0.2 |
1.4 |
| 65 |
29 |
0.5 |
3.8 |
| 66 |
8 |
0.1 |
1.0 |
| 67 |
9 |
0.2 |
1.2 |
| 68 |
10 |
0.2 |
1.3 |
| 69 |
7 |
0.1 |
0.9 |
| 7 |
15 |
0.3 |
2.0 |
| 70 |
19 |
0.3 |
2.5 |
| 71 |
6 |
0.1 |
0.8 |
| 72 |
5 |
0.1 |
0.7 |
| 73 |
8 |
0.1 |
1.0 |
| 74 |
4 |
0.1 |
0.5 |
| 75 |
5 |
0.1 |
0.7 |
| 76 |
3 |
0.1 |
0.4 |
| 77 |
5 |
0.1 |
0.7 |
| 78 |
2 |
0.0 |
0.3 |
| 79 |
1 |
0.0 |
0.1 |
| 8 |
9 |
0.2 |
1.2 |
| 80 |
2 |
0.0 |
0.3 |
| 81 |
2 |
0.0 |
0.3 |
| 9 |
13 |
0.2 |
1.7 |
| 93 |
1 |
0.0 |
0.1 |
| 95 |
1 |
0.0 |
0.1 |
| 97 |
1 |
0.0 |
0.1 |
| NA |
5073 |
86.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Stomach ulcers
b4ha <- as.factor(d[,"b4ha"])
# Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
levels(b4ha) <- list(No="1",
Yes="2")
b4ha <- ordered(b4ha, c("No", "Yes"))
new.d <- data.frame(new.d, b4ha)
new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
temp.d <- data.frame (new.d, b4ha)
result<-questionr::freq(temp.d$b4ha, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
| No |
5190 |
88.9 |
91.7 |
| Yes |
467 |
8.0 |
8.3 |
| NA |
182 |
3.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4hb <- as.factor(d[,"b4hb"])
new.d <- data.frame(new.d, b4hb)
new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
temp.d <- data.frame (new.d, b4hb)
result<-questionr::freq(temp.d$b4hb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
| 0 |
78 |
1.3 |
16.2 |
| 10 |
3 |
0.1 |
0.6 |
| 13 |
3 |
0.1 |
0.6 |
| 14 |
3 |
0.1 |
0.6 |
| 15 |
3 |
0.1 |
0.6 |
| 16 |
7 |
0.1 |
1.5 |
| 17 |
2 |
0.0 |
0.4 |
| 18 |
6 |
0.1 |
1.2 |
| 19 |
3 |
0.1 |
0.6 |
| 2 |
2 |
0.0 |
0.4 |
| 20 |
6 |
0.1 |
1.2 |
| 21 |
3 |
0.1 |
0.6 |
| 22 |
7 |
0.1 |
1.5 |
| 23 |
7 |
0.1 |
1.5 |
| 24 |
5 |
0.1 |
1.0 |
| 25 |
17 |
0.3 |
3.5 |
| 27 |
5 |
0.1 |
1.0 |
| 28 |
7 |
0.1 |
1.5 |
| 3 |
2 |
0.0 |
0.4 |
| 30 |
21 |
0.4 |
4.4 |
| 31 |
2 |
0.0 |
0.4 |
| 32 |
5 |
0.1 |
1.0 |
| 33 |
1 |
0.0 |
0.2 |
| 34 |
3 |
0.1 |
0.6 |
| 35 |
23 |
0.4 |
4.8 |
| 36 |
5 |
0.1 |
1.0 |
| 37 |
2 |
0.0 |
0.4 |
| 38 |
4 |
0.1 |
0.8 |
| 39 |
4 |
0.1 |
0.8 |
| 40 |
22 |
0.4 |
4.6 |
| 41 |
3 |
0.1 |
0.6 |
| 42 |
4 |
0.1 |
0.8 |
| 43 |
3 |
0.1 |
0.6 |
| 44 |
2 |
0.0 |
0.4 |
| 45 |
27 |
0.5 |
5.6 |
| 46 |
3 |
0.1 |
0.6 |
| 47 |
3 |
0.1 |
0.6 |
| 48 |
7 |
0.1 |
1.5 |
| 49 |
2 |
0.0 |
0.4 |
| 50 |
24 |
0.4 |
5.0 |
| 51 |
1 |
0.0 |
0.2 |
| 52 |
6 |
0.1 |
1.2 |
| 53 |
2 |
0.0 |
0.4 |
| 54 |
3 |
0.1 |
0.6 |
| 55 |
9 |
0.2 |
1.9 |
| 56 |
4 |
0.1 |
0.8 |
| 57 |
6 |
0.1 |
1.2 |
| 58 |
6 |
0.1 |
1.2 |
| 59 |
4 |
0.1 |
0.8 |
| 60 |
17 |
0.3 |
3.5 |
| 61 |
3 |
0.1 |
0.6 |
| 62 |
12 |
0.2 |
2.5 |
| 63 |
8 |
0.1 |
1.7 |
| 64 |
5 |
0.1 |
1.0 |
| 65 |
8 |
0.1 |
1.7 |
| 66 |
3 |
0.1 |
0.6 |
| 67 |
8 |
0.1 |
1.7 |
| 68 |
6 |
0.1 |
1.2 |
| 69 |
4 |
0.1 |
0.8 |
| 7 |
1 |
0.0 |
0.2 |
| 70 |
5 |
0.1 |
1.0 |
| 71 |
3 |
0.1 |
0.6 |
| 72 |
5 |
0.1 |
1.0 |
| 73 |
2 |
0.0 |
0.4 |
| 74 |
2 |
0.0 |
0.4 |
| 76 |
3 |
0.1 |
0.6 |
| 8 |
1 |
0.0 |
0.2 |
| 80 |
1 |
0.0 |
0.2 |
| 83 |
1 |
0.0 |
0.2 |
| 9 |
2 |
0.0 |
0.4 |
| 94 |
1 |
0.0 |
0.2 |
| NA |
5358 |
91.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Crohn's Disease
b4ia <- as.factor(d[,"b4ia"])
# Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
levels(b4ia) <- list(No="1",
Yes="2")
b4ia <- ordered(b4ia, c("No", "Yes"))
new.d <- data.frame(new.d, b4ia)
new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
temp.d <- data.frame (new.d, b4ia)
result<-questionr::freq(temp.d$b4ia, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
| No |
5435 |
93.1 |
96.4 |
| Yes |
204 |
3.5 |
3.6 |
| NA |
200 |
3.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4ib <- as.factor(d[,"b4ib"])
new.d <- data.frame(new.d, b4ib)
new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
temp.d <- data.frame (new.d, b4ib)
result<-questionr::freq(temp.d$b4ib, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
| 12 |
1 |
0.0 |
0.6 |
| 15 |
1 |
0.0 |
0.6 |
| 2 |
2 |
0.0 |
1.2 |
| 21 |
1 |
0.0 |
0.6 |
| 22 |
2 |
0.0 |
1.2 |
| 23 |
1 |
0.0 |
0.6 |
| 24 |
1 |
0.0 |
0.6 |
| 25 |
3 |
0.1 |
1.8 |
| 26 |
1 |
0.0 |
0.6 |
| 27 |
1 |
0.0 |
0.6 |
| 28 |
1 |
0.0 |
0.6 |
| 29 |
1 |
0.0 |
0.6 |
| 30 |
1 |
0.0 |
0.6 |
| 31 |
1 |
0.0 |
0.6 |
| 32 |
4 |
0.1 |
2.3 |
| 33 |
1 |
0.0 |
0.6 |
| 34 |
1 |
0.0 |
0.6 |
| 35 |
5 |
0.1 |
2.9 |
| 36 |
1 |
0.0 |
0.6 |
| 37 |
2 |
0.0 |
1.2 |
| 38 |
2 |
0.0 |
1.2 |
| 39 |
1 |
0.0 |
0.6 |
| 40 |
13 |
0.2 |
7.6 |
| 41 |
2 |
0.0 |
1.2 |
| 43 |
1 |
0.0 |
0.6 |
| 44 |
5 |
0.1 |
2.9 |
| 45 |
5 |
0.1 |
2.9 |
| 46 |
2 |
0.0 |
1.2 |
| 47 |
1 |
0.0 |
0.6 |
| 48 |
2 |
0.0 |
1.2 |
| 50 |
11 |
0.2 |
6.4 |
| 51 |
3 |
0.1 |
1.8 |
| 52 |
3 |
0.1 |
1.8 |
| 53 |
2 |
0.0 |
1.2 |
| 54 |
3 |
0.1 |
1.8 |
| 55 |
11 |
0.2 |
6.4 |
| 56 |
4 |
0.1 |
2.3 |
| 57 |
2 |
0.0 |
1.2 |
| 58 |
7 |
0.1 |
4.1 |
| 59 |
3 |
0.1 |
1.8 |
| 60 |
8 |
0.1 |
4.7 |
| 62 |
3 |
0.1 |
1.8 |
| 63 |
6 |
0.1 |
3.5 |
| 64 |
3 |
0.1 |
1.8 |
| 65 |
5 |
0.1 |
2.9 |
| 66 |
4 |
0.1 |
2.3 |
| 67 |
2 |
0.0 |
1.2 |
| 69 |
6 |
0.1 |
3.5 |
| 70 |
5 |
0.1 |
2.9 |
| 71 |
4 |
0.1 |
2.3 |
| 74 |
1 |
0.0 |
0.6 |
| 75 |
3 |
0.1 |
1.8 |
| 76 |
2 |
0.0 |
1.2 |
| 77 |
1 |
0.0 |
0.6 |
| 79 |
2 |
0.0 |
1.2 |
| 85 |
1 |
0.0 |
0.6 |
| NA |
5668 |
97.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Diabetes
b4ja <- as.factor(d[,"b4ja"])
# Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
levels(b4ja) <- list(No="1",
Yes="2")
b4ja <- ordered(b4ja, c("No", "Yes"))
new.d <- data.frame(new.d, b4ja)
new.d <- apply_labels(new.d, b4ja = "Diabetes")
temp.d <- data.frame (new.d, b4ja)
result<-questionr::freq(temp.d$b4ja, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
| No |
3949 |
67.6 |
69.2 |
| Yes |
1754 |
30.0 |
30.8 |
| NA |
136 |
2.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4jb <- as.factor(d[,"b4jb"])
new.d <- data.frame(new.d, b4jb)
new.d <- apply_labels(new.d, b4jb = "Diabetes age")
temp.d <- data.frame (new.d, b4jb)
result<-questionr::freq(temp.d$b4jb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
| 0 |
10 |
0.2 |
0.7 |
| 10 |
1 |
0.0 |
0.1 |
| 12 |
2 |
0.0 |
0.1 |
| 13 |
2 |
0.0 |
0.1 |
| 14 |
2 |
0.0 |
0.1 |
| 15 |
2 |
0.0 |
0.1 |
| 16 |
1 |
0.0 |
0.1 |
| 17 |
5 |
0.1 |
0.3 |
| 18 |
1 |
0.0 |
0.1 |
| 19 |
2 |
0.0 |
0.1 |
| 2 |
3 |
0.1 |
0.2 |
| 20 |
4 |
0.1 |
0.3 |
| 22 |
2 |
0.0 |
0.1 |
| 24 |
2 |
0.0 |
0.1 |
| 27 |
1 |
0.0 |
0.1 |
| 28 |
4 |
0.1 |
0.3 |
| 29 |
8 |
0.1 |
0.5 |
| 3 |
2 |
0.0 |
0.1 |
| 30 |
25 |
0.4 |
1.7 |
| 31 |
1 |
0.0 |
0.1 |
| 32 |
2 |
0.0 |
0.1 |
| 34 |
2 |
0.0 |
0.1 |
| 35 |
38 |
0.7 |
2.5 |
| 36 |
5 |
0.1 |
0.3 |
| 37 |
5 |
0.1 |
0.3 |
| 38 |
13 |
0.2 |
0.9 |
| 39 |
9 |
0.2 |
0.6 |
| 40 |
72 |
1.2 |
4.8 |
| 41 |
9 |
0.2 |
0.6 |
| 42 |
23 |
0.4 |
1.5 |
| 43 |
15 |
0.3 |
1.0 |
| 44 |
17 |
0.3 |
1.1 |
| 45 |
74 |
1.3 |
4.9 |
| 46 |
15 |
0.3 |
1.0 |
| 47 |
18 |
0.3 |
1.2 |
| 48 |
24 |
0.4 |
1.6 |
| 49 |
18 |
0.3 |
1.2 |
| 5 |
1 |
0.0 |
0.1 |
| 50 |
138 |
2.4 |
9.1 |
| 51 |
27 |
0.5 |
1.8 |
| 52 |
33 |
0.6 |
2.2 |
| 53 |
34 |
0.6 |
2.2 |
| 54 |
35 |
0.6 |
2.3 |
| 55 |
120 |
2.1 |
7.9 |
| 56 |
41 |
0.7 |
2.7 |
| 57 |
34 |
0.6 |
2.2 |
| 58 |
60 |
1.0 |
4.0 |
| 59 |
37 |
0.6 |
2.4 |
| 6 |
1 |
0.0 |
0.1 |
| 60 |
115 |
2.0 |
7.6 |
| 61 |
43 |
0.7 |
2.8 |
| 62 |
60 |
1.0 |
4.0 |
| 63 |
25 |
0.4 |
1.7 |
| 64 |
34 |
0.6 |
2.2 |
| 65 |
67 |
1.1 |
4.4 |
| 66 |
22 |
0.4 |
1.5 |
| 67 |
18 |
0.3 |
1.2 |
| 68 |
20 |
0.3 |
1.3 |
| 69 |
24 |
0.4 |
1.6 |
| 70 |
38 |
0.7 |
2.5 |
| 71 |
8 |
0.1 |
0.5 |
| 72 |
9 |
0.2 |
0.6 |
| 73 |
2 |
0.0 |
0.1 |
| 74 |
4 |
0.1 |
0.3 |
| 75 |
9 |
0.2 |
0.6 |
| 76 |
3 |
0.1 |
0.2 |
| 77 |
1 |
0.0 |
0.1 |
| 78 |
3 |
0.1 |
0.2 |
| 79 |
1 |
0.0 |
0.1 |
| 80 |
1 |
0.0 |
0.1 |
| 81 |
1 |
0.0 |
0.1 |
| 89 |
1 |
0.0 |
0.1 |
| 9 |
2 |
0.0 |
0.1 |
| 94 |
1 |
0.0 |
0.1 |
| 95 |
1 |
0.0 |
0.1 |
| 97 |
1 |
0.0 |
0.1 |
| NA |
4325 |
74.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Kidney Problems
b4ka <- as.factor(d[,"b4ka"])
# Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
levels(b4ka) <- list(No="1",
Yes="2")
b4ka <- ordered(b4ka, c("No", "Yes"))
new.d <- data.frame(new.d, b4ka)
new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
temp.d <- data.frame (new.d, b4ka)
result<-questionr::freq(temp.d$b4ka, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
| No |
5366 |
91.9 |
94.2 |
| Yes |
328 |
5.6 |
5.8 |
| NA |
145 |
2.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4kb <- as.factor(d[,"b4kb"])
new.d <- data.frame(new.d, b4kb)
new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
temp.d <- data.frame (new.d, b4kb)
result<-questionr::freq(temp.d$b4kb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
| 1 |
1 |
0.0 |
0.4 |
| 11 |
1 |
0.0 |
0.4 |
| 15 |
4 |
0.1 |
1.5 |
| 16 |
1 |
0.0 |
0.4 |
| 17 |
1 |
0.0 |
0.4 |
| 20 |
1 |
0.0 |
0.4 |
| 24 |
1 |
0.0 |
0.4 |
| 29 |
1 |
0.0 |
0.4 |
| 3 |
1 |
0.0 |
0.4 |
| 30 |
1 |
0.0 |
0.4 |
| 32 |
1 |
0.0 |
0.4 |
| 34 |
1 |
0.0 |
0.4 |
| 35 |
3 |
0.1 |
1.1 |
| 38 |
1 |
0.0 |
0.4 |
| 40 |
8 |
0.1 |
3.0 |
| 42 |
1 |
0.0 |
0.4 |
| 44 |
3 |
0.1 |
1.1 |
| 45 |
4 |
0.1 |
1.5 |
| 46 |
2 |
0.0 |
0.8 |
| 47 |
2 |
0.0 |
0.8 |
| 48 |
4 |
0.1 |
1.5 |
| 49 |
1 |
0.0 |
0.4 |
| 5 |
2 |
0.0 |
0.8 |
| 50 |
14 |
0.2 |
5.3 |
| 51 |
4 |
0.1 |
1.5 |
| 52 |
3 |
0.1 |
1.1 |
| 53 |
7 |
0.1 |
2.7 |
| 54 |
9 |
0.2 |
3.4 |
| 55 |
14 |
0.2 |
5.3 |
| 56 |
6 |
0.1 |
2.3 |
| 57 |
8 |
0.1 |
3.0 |
| 58 |
7 |
0.1 |
2.7 |
| 59 |
6 |
0.1 |
2.3 |
| 60 |
13 |
0.2 |
4.9 |
| 61 |
9 |
0.2 |
3.4 |
| 62 |
13 |
0.2 |
4.9 |
| 63 |
5 |
0.1 |
1.9 |
| 64 |
9 |
0.2 |
3.4 |
| 65 |
16 |
0.3 |
6.1 |
| 66 |
6 |
0.1 |
2.3 |
| 67 |
9 |
0.2 |
3.4 |
| 68 |
9 |
0.2 |
3.4 |
| 69 |
6 |
0.1 |
2.3 |
| 7 |
1 |
0.0 |
0.4 |
| 70 |
12 |
0.2 |
4.6 |
| 71 |
3 |
0.1 |
1.1 |
| 72 |
5 |
0.1 |
1.9 |
| 73 |
2 |
0.0 |
0.8 |
| 74 |
5 |
0.1 |
1.9 |
| 75 |
6 |
0.1 |
2.3 |
| 76 |
2 |
0.0 |
0.8 |
| 77 |
4 |
0.1 |
1.5 |
| 78 |
1 |
0.0 |
0.4 |
| 80 |
1 |
0.0 |
0.4 |
| 83 |
1 |
0.0 |
0.4 |
| 95 |
1 |
0.0 |
0.4 |
| NA |
5576 |
95.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Cirrhosis, liver damage
b4la <- as.factor(d[,"b4la"])
# Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
levels(b4la) <- list(No="1",
Yes="2")
b4la <- ordered(b4la, c("No", "Yes"))
new.d <- data.frame(new.d, b4la)
new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
temp.d <- data.frame (new.d, b4la)
result<-questionr::freq(temp.d$b4la, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
| No |
5565 |
95.3 |
98.1 |
| Yes |
109 |
1.9 |
1.9 |
| NA |
165 |
2.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4lb <- as.factor(d[,"b4lb"])
new.d <- data.frame(new.d, b4lb)
new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
temp.d <- data.frame (new.d, b4lb)
result<-questionr::freq(temp.d$b4lb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
| 18 |
1 |
0.0 |
1.2 |
| 21 |
1 |
0.0 |
1.2 |
| 25 |
1 |
0.0 |
1.2 |
| 3 |
1 |
0.0 |
1.2 |
| 34 |
1 |
0.0 |
1.2 |
| 35 |
1 |
0.0 |
1.2 |
| 39 |
1 |
0.0 |
1.2 |
| 40 |
2 |
0.0 |
2.5 |
| 42 |
1 |
0.0 |
1.2 |
| 43 |
3 |
0.1 |
3.7 |
| 44 |
1 |
0.0 |
1.2 |
| 45 |
6 |
0.1 |
7.4 |
| 47 |
1 |
0.0 |
1.2 |
| 48 |
2 |
0.0 |
2.5 |
| 49 |
1 |
0.0 |
1.2 |
| 50 |
5 |
0.1 |
6.2 |
| 51 |
3 |
0.1 |
3.7 |
| 52 |
1 |
0.0 |
1.2 |
| 53 |
1 |
0.0 |
1.2 |
| 54 |
2 |
0.0 |
2.5 |
| 55 |
4 |
0.1 |
4.9 |
| 56 |
1 |
0.0 |
1.2 |
| 57 |
1 |
0.0 |
1.2 |
| 58 |
1 |
0.0 |
1.2 |
| 60 |
11 |
0.2 |
13.6 |
| 61 |
1 |
0.0 |
1.2 |
| 63 |
1 |
0.0 |
1.2 |
| 64 |
1 |
0.0 |
1.2 |
| 65 |
5 |
0.1 |
6.2 |
| 66 |
4 |
0.1 |
4.9 |
| 67 |
3 |
0.1 |
3.7 |
| 68 |
2 |
0.0 |
2.5 |
| 69 |
4 |
0.1 |
4.9 |
| 70 |
1 |
0.0 |
1.2 |
| 73 |
1 |
0.0 |
1.2 |
| 74 |
2 |
0.0 |
2.5 |
| 77 |
1 |
0.0 |
1.2 |
| 81 |
1 |
0.0 |
1.2 |
| NA |
5758 |
98.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Arthritis
b4ma <- as.factor(d[,"b4ma"])
# Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
levels(b4ma) <- list(No="1",
Yes="2")
b4ma <- ordered(b4ma, c("No", "Yes"))
new.d <- data.frame(new.d, b4ma)
new.d <- apply_labels(new.d, b4ma = "Arthritis")
temp.d <- data.frame (new.d, b4ma)
result<-questionr::freq(temp.d$b4ma, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
| No |
4977 |
85.2 |
87.8 |
| Yes |
691 |
11.8 |
12.2 |
| NA |
171 |
2.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4mb <- as.factor(d[,"b4mb"])
new.d <- data.frame(new.d, b4mb)
new.d <- apply_labels(new.d, b4mb = "Arthritis age")
temp.d <- data.frame (new.d, b4mb)
result<-questionr::freq(temp.d$b4mb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
| 1 |
1 |
0.0 |
0.2 |
| 10 |
1 |
0.0 |
0.2 |
| 15 |
1 |
0.0 |
0.2 |
| 17 |
5 |
0.1 |
0.9 |
| 18 |
1 |
0.0 |
0.2 |
| 19 |
1 |
0.0 |
0.2 |
| 2 |
1 |
0.0 |
0.2 |
| 20 |
4 |
0.1 |
0.7 |
| 21 |
2 |
0.0 |
0.3 |
| 22 |
1 |
0.0 |
0.2 |
| 23 |
2 |
0.0 |
0.3 |
| 24 |
1 |
0.0 |
0.2 |
| 25 |
3 |
0.1 |
0.5 |
| 28 |
2 |
0.0 |
0.3 |
| 30 |
15 |
0.3 |
2.6 |
| 32 |
5 |
0.1 |
0.9 |
| 33 |
1 |
0.0 |
0.2 |
| 34 |
1 |
0.0 |
0.2 |
| 35 |
15 |
0.3 |
2.6 |
| 36 |
7 |
0.1 |
1.2 |
| 38 |
4 |
0.1 |
0.7 |
| 39 |
5 |
0.1 |
0.9 |
| 4 |
2 |
0.0 |
0.3 |
| 40 |
30 |
0.5 |
5.1 |
| 41 |
6 |
0.1 |
1.0 |
| 42 |
6 |
0.1 |
1.0 |
| 43 |
6 |
0.1 |
1.0 |
| 44 |
6 |
0.1 |
1.0 |
| 45 |
30 |
0.5 |
5.1 |
| 46 |
2 |
0.0 |
0.3 |
| 47 |
4 |
0.1 |
0.7 |
| 48 |
10 |
0.2 |
1.7 |
| 49 |
4 |
0.1 |
0.7 |
| 50 |
55 |
0.9 |
9.4 |
| 51 |
9 |
0.2 |
1.5 |
| 52 |
13 |
0.2 |
2.2 |
| 53 |
10 |
0.2 |
1.7 |
| 54 |
13 |
0.2 |
2.2 |
| 55 |
39 |
0.7 |
6.6 |
| 56 |
12 |
0.2 |
2.0 |
| 57 |
10 |
0.2 |
1.7 |
| 58 |
16 |
0.3 |
2.7 |
| 59 |
8 |
0.1 |
1.4 |
| 60 |
62 |
1.1 |
10.5 |
| 61 |
8 |
0.1 |
1.4 |
| 62 |
20 |
0.3 |
3.4 |
| 63 |
14 |
0.2 |
2.4 |
| 64 |
14 |
0.2 |
2.4 |
| 65 |
28 |
0.5 |
4.8 |
| 66 |
7 |
0.1 |
1.2 |
| 67 |
10 |
0.2 |
1.7 |
| 68 |
8 |
0.1 |
1.4 |
| 69 |
6 |
0.1 |
1.0 |
| 7 |
1 |
0.0 |
0.2 |
| 70 |
15 |
0.3 |
2.6 |
| 71 |
4 |
0.1 |
0.7 |
| 72 |
5 |
0.1 |
0.9 |
| 73 |
2 |
0.0 |
0.3 |
| 74 |
2 |
0.0 |
0.3 |
| 75 |
4 |
0.1 |
0.7 |
| 76 |
1 |
0.0 |
0.2 |
| 77 |
2 |
0.0 |
0.3 |
| 78 |
1 |
0.0 |
0.2 |
| 80 |
1 |
0.0 |
0.2 |
| 81 |
1 |
0.0 |
0.2 |
| 85 |
1 |
0.0 |
0.2 |
| 94 |
1 |
0.0 |
0.2 |
| NA |
5251 |
89.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Dementia
b4na <- as.factor(d[,"b4na"])
# Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
levels(b4na) <- list(No="1",
Yes="2")
b4na <- ordered(b4na, c("No", "Yes"))
new.d <- data.frame(new.d, b4na)
new.d <- apply_labels(new.d, b4na = "Dementia")
temp.d <- data.frame (new.d, b4na)
result<-questionr::freq(temp.d$b4na, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
| No |
5632 |
96.5 |
98.8 |
| Yes |
68 |
1.2 |
1.2 |
| NA |
139 |
2.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4nb <- as.factor(d[,"b4nb"])
new.d <- data.frame(new.d, b4nb)
new.d <- apply_labels(new.d, b4nb = "Dementia age")
temp.d <- data.frame (new.d, b4nb)
result<-questionr::freq(temp.d$b4nb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
| 10 |
1 |
0.0 |
2.2 |
| 29 |
1 |
0.0 |
2.2 |
| 53 |
1 |
0.0 |
2.2 |
| 57 |
1 |
0.0 |
2.2 |
| 59 |
2 |
0.0 |
4.4 |
| 60 |
1 |
0.0 |
2.2 |
| 62 |
3 |
0.1 |
6.7 |
| 63 |
3 |
0.1 |
6.7 |
| 64 |
1 |
0.0 |
2.2 |
| 65 |
2 |
0.0 |
4.4 |
| 66 |
1 |
0.0 |
2.2 |
| 67 |
1 |
0.0 |
2.2 |
| 68 |
1 |
0.0 |
2.2 |
| 69 |
1 |
0.0 |
2.2 |
| 70 |
10 |
0.2 |
22.2 |
| 72 |
4 |
0.1 |
8.9 |
| 73 |
2 |
0.0 |
4.4 |
| 74 |
1 |
0.0 |
2.2 |
| 75 |
4 |
0.1 |
8.9 |
| 79 |
1 |
0.0 |
2.2 |
| 80 |
1 |
0.0 |
2.2 |
| 84 |
1 |
0.0 |
2.2 |
| 85 |
1 |
0.0 |
2.2 |
| NA |
5794 |
99.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Depression
b4oa <- as.factor(d[,"b4oa"])
# Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
levels(b4oa) <- list(No="1",
Yes="2")
b4oa <- ordered(b4oa, c("No", "Yes"))
new.d <- data.frame(new.d, b4oa)
new.d <- apply_labels(new.d, b4oa = "Depression")
temp.d <- data.frame (new.d, b4oa)
result<-questionr::freq(temp.d$b4oa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
| No |
5002 |
85.7 |
88.1 |
| Yes |
676 |
11.6 |
11.9 |
| NA |
161 |
2.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4ob <- as.factor(d[,"b4ob"])
new.d <- data.frame(new.d, b4ob)
new.d <- apply_labels(new.d, b4ob = "Depression age")
temp.d <- data.frame (new.d, b4ob)
result<-questionr::freq(temp.d$b4ob, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
| 1 |
1 |
0.0 |
0.2 |
| 10 |
1 |
0.0 |
0.2 |
| 14 |
1 |
0.0 |
0.2 |
| 15 |
1 |
0.0 |
0.2 |
| 16 |
1 |
0.0 |
0.2 |
| 17 |
1 |
0.0 |
0.2 |
| 18 |
1 |
0.0 |
0.2 |
| 19 |
8 |
0.1 |
1.5 |
| 20 |
10 |
0.2 |
1.9 |
| 21 |
7 |
0.1 |
1.3 |
| 22 |
2 |
0.0 |
0.4 |
| 23 |
1 |
0.0 |
0.2 |
| 24 |
1 |
0.0 |
0.2 |
| 25 |
10 |
0.2 |
1.9 |
| 26 |
3 |
0.1 |
0.6 |
| 27 |
1 |
0.0 |
0.2 |
| 28 |
9 |
0.2 |
1.7 |
| 29 |
2 |
0.0 |
0.4 |
| 3 |
1 |
0.0 |
0.2 |
| 30 |
10 |
0.2 |
1.9 |
| 31 |
1 |
0.0 |
0.2 |
| 32 |
6 |
0.1 |
1.1 |
| 33 |
2 |
0.0 |
0.4 |
| 34 |
3 |
0.1 |
0.6 |
| 35 |
20 |
0.3 |
3.8 |
| 36 |
5 |
0.1 |
1.0 |
| 37 |
4 |
0.1 |
0.8 |
| 38 |
7 |
0.1 |
1.3 |
| 39 |
2 |
0.0 |
0.4 |
| 4 |
1 |
0.0 |
0.2 |
| 40 |
23 |
0.4 |
4.4 |
| 41 |
3 |
0.1 |
0.6 |
| 42 |
10 |
0.2 |
1.9 |
| 43 |
8 |
0.1 |
1.5 |
| 44 |
6 |
0.1 |
1.1 |
| 45 |
18 |
0.3 |
3.4 |
| 46 |
7 |
0.1 |
1.3 |
| 47 |
8 |
0.1 |
1.5 |
| 48 |
13 |
0.2 |
2.5 |
| 49 |
7 |
0.1 |
1.3 |
| 5 |
1 |
0.0 |
0.2 |
| 50 |
41 |
0.7 |
7.8 |
| 51 |
7 |
0.1 |
1.3 |
| 52 |
12 |
0.2 |
2.3 |
| 53 |
8 |
0.1 |
1.5 |
| 54 |
8 |
0.1 |
1.5 |
| 55 |
27 |
0.5 |
5.2 |
| 56 |
8 |
0.1 |
1.5 |
| 57 |
12 |
0.2 |
2.3 |
| 58 |
15 |
0.3 |
2.9 |
| 59 |
7 |
0.1 |
1.3 |
| 6 |
1 |
0.0 |
0.2 |
| 60 |
26 |
0.4 |
5.0 |
| 61 |
12 |
0.2 |
2.3 |
| 62 |
9 |
0.2 |
1.7 |
| 63 |
6 |
0.1 |
1.1 |
| 64 |
7 |
0.1 |
1.3 |
| 65 |
23 |
0.4 |
4.4 |
| 66 |
10 |
0.2 |
1.9 |
| 67 |
4 |
0.1 |
0.8 |
| 68 |
13 |
0.2 |
2.5 |
| 69 |
6 |
0.1 |
1.1 |
| 7 |
2 |
0.0 |
0.4 |
| 70 |
14 |
0.2 |
2.7 |
| 72 |
3 |
0.1 |
0.6 |
| 73 |
4 |
0.1 |
0.8 |
| 74 |
2 |
0.0 |
0.4 |
| 75 |
1 |
0.0 |
0.2 |
| 76 |
2 |
0.0 |
0.4 |
| 79 |
1 |
0.0 |
0.2 |
| 8 |
1 |
0.0 |
0.2 |
| 9 |
1 |
0.0 |
0.2 |
| 91 |
1 |
0.0 |
0.2 |
| 96 |
1 |
0.0 |
0.2 |
| 98 |
2 |
0.0 |
0.4 |
| NA |
5315 |
91.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# AIDS
b4pa <- as.factor(d[,"b4pa"])
# Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
levels(b4pa) <- list(No="1",
Yes="2")
b4pa <- ordered(b4pa, c("No", "Yes"))
new.d <- data.frame(new.d, b4pa)
new.d <- apply_labels(new.d, b4pa = "AIDS")
temp.d <- data.frame (new.d, b4pa)
result<-questionr::freq(temp.d$b4pa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
| No |
5604 |
96.0 |
98.8 |
| Yes |
69 |
1.2 |
1.2 |
| NA |
166 |
2.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4pb <- as.factor(d[,"b4pb"])
new.d <- data.frame(new.d, b4pb)
new.d <- apply_labels(new.d, b4pb = "AIDS age")
temp.d <- data.frame (new.d, b4pb)
result<-questionr::freq(temp.d$b4pb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
| 0 |
82 |
1.4 |
62.1 |
| 25 |
2 |
0.0 |
1.5 |
| 28 |
1 |
0.0 |
0.8 |
| 30 |
3 |
0.1 |
2.3 |
| 31 |
1 |
0.0 |
0.8 |
| 32 |
1 |
0.0 |
0.8 |
| 33 |
1 |
0.0 |
0.8 |
| 35 |
2 |
0.0 |
1.5 |
| 36 |
2 |
0.0 |
1.5 |
| 38 |
1 |
0.0 |
0.8 |
| 39 |
1 |
0.0 |
0.8 |
| 40 |
4 |
0.1 |
3.0 |
| 42 |
1 |
0.0 |
0.8 |
| 44 |
1 |
0.0 |
0.8 |
| 45 |
2 |
0.0 |
1.5 |
| 47 |
1 |
0.0 |
0.8 |
| 48 |
2 |
0.0 |
1.5 |
| 49 |
2 |
0.0 |
1.5 |
| 50 |
4 |
0.1 |
3.0 |
| 51 |
2 |
0.0 |
1.5 |
| 54 |
1 |
0.0 |
0.8 |
| 55 |
2 |
0.0 |
1.5 |
| 59 |
2 |
0.0 |
1.5 |
| 60 |
3 |
0.1 |
2.3 |
| 61 |
1 |
0.0 |
0.8 |
| 63 |
1 |
0.0 |
0.8 |
| 65 |
1 |
0.0 |
0.8 |
| 66 |
1 |
0.0 |
0.8 |
| 69 |
1 |
0.0 |
0.8 |
| 7 |
1 |
0.0 |
0.8 |
| 9 |
1 |
0.0 |
0.8 |
| 90 |
1 |
0.0 |
0.8 |
| NA |
5707 |
97.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# Other Cancer
b4qa <- as.factor(d[,"b4qa"])
# Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
levels(b4qa) <- list(No="1",
Yes="2")
b4qa <- ordered(b4qa, c("No", "Yes"))
new.d <- data.frame(new.d, b4qa)
new.d <- apply_labels(new.d, b4qa = "Other Cancer")
temp.d <- data.frame (new.d, b4qa)
result<-questionr::freq(temp.d$b4qa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
| No |
5204 |
89.1 |
93.3 |
| Yes |
376 |
6.4 |
6.7 |
| NA |
259 |
4.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
b4qb <- as.factor(d[,"b4qb"])
new.d <- data.frame(new.d, b4qb)
new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
temp.d <- data.frame (new.d, b4qb)
result<-questionr::freq(temp.d$b4qb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
| 08 |
1 |
0.0 |
0.3 |
| 10 |
1 |
0.0 |
0.3 |
| 12 |
1 |
0.0 |
0.3 |
| 16 |
1 |
0.0 |
0.3 |
| 18 |
2 |
0.0 |
0.6 |
| 19 |
2 |
0.0 |
0.6 |
| 2 |
1 |
0.0 |
0.3 |
| 20 |
1 |
0.0 |
0.3 |
| 21 |
1 |
0.0 |
0.3 |
| 22 |
1 |
0.0 |
0.3 |
| 23 |
1 |
0.0 |
0.3 |
| 24 |
1 |
0.0 |
0.3 |
| 3 |
1 |
0.0 |
0.3 |
| 30 |
3 |
0.1 |
0.9 |
| 32 |
1 |
0.0 |
0.3 |
| 35 |
1 |
0.0 |
0.3 |
| 36 |
1 |
0.0 |
0.3 |
| 38 |
1 |
0.0 |
0.3 |
| 39 |
1 |
0.0 |
0.3 |
| 40 |
3 |
0.1 |
0.9 |
| 41 |
2 |
0.0 |
0.6 |
| 42 |
4 |
0.1 |
1.2 |
| 43 |
4 |
0.1 |
1.2 |
| 44 |
7 |
0.1 |
2.1 |
| 45 |
2 |
0.0 |
0.6 |
| 47 |
2 |
0.0 |
0.6 |
| 48 |
3 |
0.1 |
0.9 |
| 49 |
9 |
0.2 |
2.7 |
| 50 |
10 |
0.2 |
3.0 |
| 51 |
4 |
0.1 |
1.2 |
| 52 |
3 |
0.1 |
0.9 |
| 53 |
5 |
0.1 |
1.5 |
| 54 |
11 |
0.2 |
3.3 |
| 55 |
14 |
0.2 |
4.2 |
| 56 |
14 |
0.2 |
4.2 |
| 57 |
7 |
0.1 |
2.1 |
| 58 |
20 |
0.3 |
6.0 |
| 59 |
6 |
0.1 |
1.8 |
| 60 |
23 |
0.4 |
6.9 |
| 61 |
15 |
0.3 |
4.5 |
| 62 |
12 |
0.2 |
3.6 |
| 63 |
13 |
0.2 |
3.9 |
| 64 |
9 |
0.2 |
2.7 |
| 65 |
12 |
0.2 |
3.6 |
| 66 |
10 |
0.2 |
3.0 |
| 67 |
9 |
0.2 |
2.7 |
| 68 |
13 |
0.2 |
3.9 |
| 69 |
11 |
0.2 |
3.3 |
| 7 |
1 |
0.0 |
0.3 |
| 70 |
11 |
0.2 |
3.3 |
| 71 |
5 |
0.1 |
1.5 |
| 72 |
7 |
0.1 |
2.1 |
| 73 |
5 |
0.1 |
1.5 |
| 74 |
8 |
0.1 |
2.4 |
| 75 |
3 |
0.1 |
0.9 |
| 76 |
4 |
0.1 |
1.2 |
| 77 |
2 |
0.0 |
0.6 |
| 78 |
2 |
0.0 |
0.6 |
| 79 |
1 |
0.0 |
0.3 |
| 80 |
2 |
0.0 |
0.6 |
| 81 |
2 |
0.0 |
0.6 |
| 87 |
1 |
0.0 |
0.3 |
| 9 |
1 |
0.0 |
0.3 |
| NA |
5504 |
94.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B4Q Other Cancer
b4qother <- d[,"b4qother"]
new.d <- data.frame(new.d, b4qother)
new.d <- apply_labels(new.d, b4qother = "b4qother")
temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B4Q Other
| #NAME? |
1 |
0.0 |
0.3 |
| 3rd stage lung cancer |
1 |
0.0 |
0.3 |
| AML. |
1 |
0.0 |
0.3 |
| Anal |
1 |
0.0 |
0.3 |
| Anal cancer |
1 |
0.0 |
0.3 |
| Anemia. |
1 |
0.0 |
0.3 |
| Benign melanoma |
1 |
0.0 |
0.3 |
| Bladder |
10 |
0.2 |
2.8 |
| Bladder cancer |
3 |
0.1 |
0.8 |
| Bladder Cancer |
2 |
0.0 |
0.6 |
| Bladder cancer. |
2 |
0.0 |
0.6 |
| Bladder. |
1 |
0.0 |
0.3 |
| Blood cancer |
1 |
0.0 |
0.3 |
| Bone |
1 |
0.0 |
0.3 |
| Bone cancer |
1 |
0.0 |
0.3 |
| Bone marrow |
1 |
0.0 |
0.3 |
| Bone marrow cancer |
1 |
0.0 |
0.3 |
| Bone marrow cancer. |
1 |
0.0 |
0.3 |
| Bone. |
2 |
0.0 |
0.6 |
| Breast |
1 |
0.0 |
0.3 |
| breast cancer |
1 |
0.0 |
0.3 |
| Breast cancer |
2 |
0.0 |
0.6 |
| Cancer is now affecting my bones. |
1 |
0.0 |
0.3 |
| Cancer of my vocal cords |
1 |
0.0 |
0.3 |
| Cancerous colon polyp. |
1 |
0.0 |
0.3 |
| Cancerous tumor on right kidney, 2 centimeters cut away. |
1 |
0.0 |
0.3 |
| Cancerous tumor on right Kidney, kidney removed |
1 |
0.0 |
0.3 |
| Check now on bile ducts. |
1 |
0.0 |
0.3 |
| Chronic lymphocytic leukemia |
1 |
0.0 |
0.3 |
| CLL |
3 |
0.1 |
0.8 |
| CLL 2018/Lung cancer |
1 |
0.0 |
0.3 |
| CLL. |
1 |
0.0 |
0.3 |
| CML Chronic Myeloid Leukemia |
1 |
0.0 |
0.3 |
| CML, Polymyalgia Rheumatic bladder, and Amyloidosis of urinary bladder |
1 |
0.0 |
0.3 |
| CML. |
1 |
0.0 |
0.3 |
| CO |
1 |
0.0 |
0.3 |
| Colo rectal cancer |
1 |
0.0 |
0.3 |
| Colo-rectal Cancer |
1 |
0.0 |
0.3 |
| colon |
3 |
0.1 |
0.8 |
| Colon |
30 |
0.5 |
8.4 |
| Colon (2000). |
1 |
0.0 |
0.3 |
| Colon (polyps) |
1 |
0.0 |
0.3 |
| Colon (self) |
1 |
0.0 |
0.3 |
| Colon and left and right kidney |
1 |
0.0 |
0.3 |
| colon cancer |
1 |
0.0 |
0.3 |
| Colon cancer |
9 |
0.2 |
2.5 |
| Colon Cancer |
3 |
0.1 |
0.8 |
| Colon cancer. |
3 |
0.1 |
0.8 |
| Colon Dr. removed it and that’s over |
1 |
0.0 |
0.3 |
| Colon. |
2 |
0.0 |
0.6 |
| Colorectal |
2 |
0.0 |
0.6 |
| Colorectal cancer. |
1 |
0.0 |
0.3 |
| COPD |
2 |
0.0 |
0.6 |
| Do not know dates on prior questions |
1 |
0.0 |
0.3 |
| Duodenum. |
1 |
0.0 |
0.3 |
| esophageal |
1 |
0.0 |
0.3 |
| Esophageal |
1 |
0.0 |
0.3 |
| esophagus |
1 |
0.0 |
0.3 |
| Esophagus, lung cancer |
1 |
0.0 |
0.3 |
| GIST |
1 |
0.0 |
0.3 |
| Glast cancer |
1 |
0.0 |
0.3 |
| Glaucoma cancer |
1 |
0.0 |
0.3 |
| Growth on bladder (unknown) |
1 |
0.0 |
0.3 |
| Growth on Kidney |
1 |
0.0 |
0.3 |
| Has asbestos |
1 |
0.0 |
0.3 |
| Head and neck (throat). |
1 |
0.0 |
0.3 |
| Head and Neck (Tounge cancer in 2007) |
1 |
0.0 |
0.3 |
| Hepatitis C |
1 |
0.0 |
0.3 |
| HIV |
1 |
0.0 |
0.3 |
| HIV positive |
1 |
0.0 |
0.3 |
| HIV positive, no detected |
1 |
0.0 |
0.3 |
| HIV. |
1 |
0.0 |
0.3 |
| HPV |
1 |
0.0 |
0.3 |
| Hypertropic Bowen’s disease |
1 |
0.0 |
0.3 |
| I do not know, maybe |
1 |
0.0 |
0.3 |
| In the family (call me). |
1 |
0.0 |
0.3 |
| Invasive squamous cell carcinoma (on finger-amputation of distal portion of LF. index finger). |
1 |
0.0 |
0.3 |
| Kalsom |
1 |
0.0 |
0.3 |
| Kaposis Sarcoma |
1 |
0.0 |
0.3 |
| kidney |
2 |
0.0 |
0.6 |
| Kidney |
16 |
0.3 |
4.5 |
| KIDNEY |
1 |
0.0 |
0.3 |
| Kidney (left kidney removed). |
1 |
0.0 |
0.3 |
| kidney cancer |
1 |
0.0 |
0.3 |
| Kidney cancer |
3 |
0.1 |
0.8 |
| Kidney Cancer |
6 |
0.1 |
1.7 |
| Kidney cancer in 2016 |
1 |
0.0 |
0.3 |
| Kidney cancer. |
2 |
0.0 |
0.6 |
| Kidney had kidney ablasion. |
1 |
0.0 |
0.3 |
| Kidney tumor |
1 |
0.0 |
0.3 |
| Kidney-colon. |
1 |
0.0 |
0.3 |
| Kidney-partial nephrectomy. |
1 |
0.0 |
0.3 |
| Kidney-prostate |
1 |
0.0 |
0.3 |
| Kidney. |
4 |
0.1 |
1.1 |
| Left kidney |
1 |
0.0 |
0.3 |
| Left kidney was taken (believe to be cancerous) after taken and tested it was not cancerous. So I have only one now, right side. |
1 |
0.0 |
0.3 |
| Left kidney. |
1 |
0.0 |
0.3 |
| Leukemia |
2 |
0.0 |
0.6 |
| Leukemia (CML). |
1 |
0.0 |
0.3 |
| Liver |
3 |
0.1 |
0.8 |
| Liver cancer colon cancer |
1 |
0.0 |
0.3 |
| Liver had transplant 71 years |
1 |
0.0 |
0.3 |
| Liver mass (7-11-14) |
1 |
0.0 |
0.3 |
| LMS |
1 |
0.0 |
0.3 |
| Lt. lung removed 97. |
1 |
0.0 |
0.3 |
| Lump in right side of neck |
1 |
0.0 |
0.3 |
| lung |
3 |
0.1 |
0.8 |
| Lung |
14 |
0.2 |
3.9 |
| Lung (left 2016) right-present |
1 |
0.0 |
0.3 |
| lung cancer |
1 |
0.0 |
0.3 |
| Lung cancer |
5 |
0.1 |
1.4 |
| Lung cancer-stage 4. |
1 |
0.0 |
0.3 |
| Lung cancer. |
1 |
0.0 |
0.3 |
| Lung caner |
1 |
0.0 |
0.3 |
| Lung, —- on kidney, prostate |
1 |
0.0 |
0.3 |
| Lung, esophagus |
1 |
0.0 |
0.3 |
| Lung/GIST tumor. |
1 |
0.0 |
0.3 |
| Lupus |
1 |
0.0 |
0.3 |
| Lymphoid cancer stage 4. |
1 |
0.0 |
0.3 |
| Lymphoma |
7 |
0.1 |
2.0 |
| Lymphoma 1999-leukemia 2021. |
1 |
0.0 |
0.3 |
| Lymphoma low grade |
1 |
0.0 |
0.3 |
| Lymphoma. |
1 |
0.0 |
0.3 |
| Lymphoma/Colon |
1 |
0.0 |
0.3 |
| lyphama |
1 |
0.0 |
0.3 |
| MAL and Lymphoma |
1 |
0.0 |
0.3 |
| Malignant tumor in bladder. |
1 |
0.0 |
0.3 |
| MDS |
1 |
0.0 |
0.3 |
| Melanoma |
2 |
0.0 |
0.6 |
| Meningioma |
1 |
0.0 |
0.3 |
| Metastatic Papillary Thyroid Cancer |
1 |
0.0 |
0.3 |
| MPD and AML |
1 |
0.0 |
0.3 |
| Multiple myeloma |
1 |
0.0 |
0.3 |
| multiple myeloma |
1 |
0.0 |
0.3 |
| Multiple myeloma |
1 |
0.0 |
0.3 |
| Multiple Myeloma |
7 |
0.1 |
2.0 |
| Multiple Myloma |
1 |
0.0 |
0.3 |
| My left kidney was removed because a mass was found on it in April 2018, it was cancer, my kidney was removed on May 1, 2018. |
1 |
0.0 |
0.3 |
| My sister at 63, colon. |
1 |
0.0 |
0.3 |
| Myeloma |
1 |
0.0 |
0.3 |
| Myloma kidney |
1 |
0.0 |
0.3 |
| Nasal pharyngeal |
1 |
0.0 |
0.3 |
| Neck cancer |
1 |
0.0 |
0.3 |
| Neuroendocrine (Carcinoma) |
1 |
0.0 |
0.3 |
| Neuroendocrine cancer |
1 |
0.0 |
0.3 |
| NH Lymphoma, skin cancer |
1 |
0.0 |
0.3 |
| No |
2 |
0.0 |
0.6 |
| Non Hodgkin’s Lymphoma (CLL) |
1 |
0.0 |
0.3 |
| Non Hodgkins Lymphoma |
1 |
0.0 |
0.3 |
| Non-Hodgekins Lymphoma. |
1 |
0.0 |
0.3 |
| non-Hodgkin lymphoma |
1 |
0.0 |
0.3 |
| Non-Hodgkin Lymphoma |
1 |
0.0 |
0.3 |
| Non-Hodgkin’s lymphoma |
1 |
0.0 |
0.3 |
| Non-Hodgkin’s Lymphoma |
1 |
0.0 |
0.3 |
| Non-Hodgkins Lymphoma (2) |
1 |
0.0 |
0.3 |
| None. |
1 |
0.0 |
0.3 |
| Oral |
1 |
0.0 |
0.3 |
| Over active white blood cell |
1 |
0.0 |
0.3 |
| Pancreatic |
2 |
0.0 |
0.6 |
| Pancreatic and liver |
1 |
0.0 |
0.3 |
| Partial kidney removal right side |
1 |
0.0 |
0.3 |
| Partial left kidney removal |
1 |
0.0 |
0.3 |
| Pelvis and lymphoma |
1 |
0.0 |
0.3 |
| Penile Cancer |
1 |
0.0 |
0.3 |
| Posterial Sarcoma |
1 |
0.0 |
0.3 |
| Prostate |
1 |
0.0 |
0.3 |
| Prostate only |
1 |
0.0 |
0.3 |
| Rectal |
1 |
0.0 |
0.3 |
| Rectal cancer |
1 |
0.0 |
0.3 |
| Refused! |
1 |
0.0 |
0.3 |
| Renal kidney remove left side |
1 |
0.0 |
0.3 |
| Results by Dr. Libby 2018 no cancer found! |
1 |
0.0 |
0.3 |
| right kidney |
1 |
0.0 |
0.3 |
| Right kidney |
1 |
0.0 |
0.3 |
| Right kidney (neprectomy). |
1 |
0.0 |
0.3 |
| Right kidney nephrectomy |
1 |
0.0 |
0.3 |
| Right vocal cord |
1 |
0.0 |
0.3 |
| Salivary gland cancer. |
1 |
0.0 |
0.3 |
| Sarcoma-Soft tissue cancer |
1 |
0.0 |
0.3 |
| sinus and right axilary muscle |
1 |
0.0 |
0.3 |
| Skin |
2 |
0.0 |
0.6 |
| Skin cancer |
4 |
0.1 |
1.1 |
| Skin cancer stomach area |
1 |
0.0 |
0.3 |
| Skin Cancer, Breast Cancer |
1 |
0.0 |
0.3 |
| Skin cancer. |
2 |
0.0 |
0.6 |
| Skin Cancer/side effect from Kertruda treatment |
1 |
0.0 |
0.3 |
| Small cell Lung cancer |
1 |
0.0 |
0.3 |
| Small tumor on brain |
1 |
0.0 |
0.3 |
| Spread to liver and pelvic area. |
1 |
0.0 |
0.3 |
| Squamous Cell Carcinoma |
1 |
0.0 |
0.3 |
| Stage 4 lung cancer |
1 |
0.0 |
0.3 |
| Stomach cancer |
1 |
0.0 |
0.3 |
| Stomach cancer. |
1 |
0.0 |
0.3 |
| Stomach. |
2 |
0.0 |
0.6 |
| T cell lymphoma |
1 |
0.0 |
0.3 |
| testicular |
1 |
0.0 |
0.3 |
| Testicular |
1 |
0.0 |
0.3 |
| Thank God, I don’t need more |
1 |
0.0 |
0.3 |
| There was a nodule on my lungs It was removed it was cancer. |
1 |
0.0 |
0.3 |
| Throat |
6 |
0.1 |
1.7 |
| Throat and tonsils |
1 |
0.0 |
0.3 |
| throat cancer |
1 |
0.0 |
0.3 |
| Throat cancer |
1 |
0.0 |
0.3 |
| Throat cancer, vocal chord cancer |
1 |
0.0 |
0.3 |
| thyroid |
1 |
0.0 |
0.3 |
| Thyroid |
5 |
0.1 |
1.4 |
| Thyroid gland disease |
1 |
0.0 |
0.3 |
| Thyroid. |
2 |
0.0 |
0.6 |
| Tumor cancer |
1 |
0.0 |
0.3 |
| Tumor in bladder |
1 |
0.0 |
0.3 |
| Tumor on my left kidney (Nephrectomy) |
1 |
0.0 |
0.3 |
| Unknown origin |
1 |
0.0 |
0.3 |
| Vocal cord |
1 |
0.0 |
0.3 |
| Waldenstrom Non Hodgkins Lymphoma |
1 |
0.0 |
0.3 |
| Yes, prostate cancer. |
1 |
0.0 |
0.3 |
| NA |
5481 |
93.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B5: Routine care
- B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
- 1=Community health center or free clinic
- 2=Hospital (not emergency)/ urgent care clinic
- 3=Private doctor’s office
- 4=Emergency room
- 5=Veteran’s Affairs/VA
- 6=Other type of location
b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
levels(b5) <- list(Community_center_free_clinic="1",
Hospital_urgent_care_clinic="2",
Private_Dr_office="3",
ER="4",
VA="5",
Other="6")
b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
new.d <- data.frame(new.d, b5)
new.d <- apply_labels(new.d, b5 = "routine medical care")
temp.d <- data.frame (new.d, b5)
result<-questionr::freq(temp.d$b5 ,total = TRUE)
kable(result, format = "simple", align = 'l')
| Community_center_free_clinic |
380 |
6.5 |
7.1 |
| Hospital_urgent_care_clinic |
400 |
6.9 |
7.5 |
| Private_Dr_office |
3912 |
67.0 |
73.1 |
| ER |
37 |
0.6 |
0.7 |
| VA |
513 |
8.8 |
9.6 |
| Other |
108 |
1.8 |
2.0 |
| NA |
489 |
8.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
B5 Other: Routine care
b5other <- d[,"b5other"]
new.d <- data.frame(new.d, b5other)
new.d <- apply_labels(new.d, b5other = "b5other")
temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B5 Other")
B5 Other
| (Emory), Wellstar, family MD, Kennestone Family Medicine. |
1 |
0.0 |
0.4 |
| 29 Palms Navel Hospital |
1 |
0.0 |
0.4 |
| A. Ahmad, MD PLC. |
1 |
0.0 |
0.4 |
| Advance Medical Mobile Care |
1 |
0.0 |
0.4 |
| Advanced Urology |
1 |
0.0 |
0.4 |
| Advocate Aurora Health facility |
1 |
0.0 |
0.4 |
| Aenta |
1 |
0.0 |
0.4 |
| Albany health care center |
1 |
0.0 |
0.4 |
| Alpha Medical |
1 |
0.0 |
0.4 |
| Also private doctor’s office |
1 |
0.0 |
0.4 |
| Atlanta cancer care/Northside hospital |
1 |
0.0 |
0.4 |
| Atlanta Heart Specialist |
1 |
0.0 |
0.4 |
| Austin Diagnostic Clinic |
1 |
0.0 |
0.4 |
| Back specialist. |
1 |
0.0 |
0.4 |
| Baylor Hospital Dr. Joshua Fines |
1 |
0.0 |
0.4 |
| Beaumont Doctors and VA |
1 |
0.0 |
0.4 |
| Beaumont Family Medicine. |
1 |
0.0 |
0.4 |
| Ben tab |
1 |
0.0 |
0.4 |
| Braselton Clinic, Braselton GA |
1 |
0.0 |
0.4 |
| Cancer center. |
1 |
0.0 |
0.4 |
| Cancer treatment of America (CTA). |
1 |
0.0 |
0.4 |
| CDC-Doctor |
1 |
0.0 |
0.4 |
| Cedar |
1 |
0.0 |
0.4 |
| Chabert Medical Center |
1 |
0.0 |
0.4 |
| Charleston SC Hinesville Cr. |
1 |
0.0 |
0.4 |
| CIS Diabetes Specialist |
1 |
0.0 |
0.4 |
| City of Hope |
1 |
0.0 |
0.4 |
| Clinic. |
1 |
0.0 |
0.4 |
| Company doctor |
1 |
0.0 |
0.4 |
| Crescent Care Primary Providers |
1 |
0.0 |
0.4 |
| CTCA Cancer Treatment Center |
1 |
0.0 |
0.4 |
| Davis Medical GPMC ST/CA. |
1 |
0.0 |
0.4 |
| Dialyse dialysis. |
1 |
0.0 |
0.4 |
| Dialysis center |
1 |
0.0 |
0.4 |
| Doctor affiliated with hospital |
1 |
0.0 |
0.4 |
| Doctor’s office located at hospital. |
1 |
0.0 |
0.4 |
| doctors office |
1 |
0.0 |
0.4 |
| Doctors office (primary Dr) |
1 |
0.0 |
0.4 |
| Dr. Alvin Griffin |
1 |
0.0 |
0.4 |
| Dr. Chomsky |
1 |
0.0 |
0.4 |
| Dr. Feldman (Oakland Family Practice office) |
1 |
0.0 |
0.4 |
| Dr. Harper |
1 |
0.0 |
0.4 |
| Dr. Karimjee |
1 |
0.0 |
0.4 |
| Dr. Mark Clark (Cape Charles Medical Center). |
1 |
0.0 |
0.4 |
| Dr. Marvin Crawford (Piedmont) |
1 |
0.0 |
0.4 |
| Dr. Ramiz. |
1 |
0.0 |
0.4 |
| Dr. Reed Laygo |
1 |
0.0 |
0.4 |
| Eagle Landing family practice |
1 |
0.0 |
0.4 |
| Eastmont Medical Center |
1 |
0.0 |
0.4 |
| EM room |
1 |
0.0 |
0.4 |
| Emomory Health Care La Grange GA |
1 |
0.0 |
0.4 |
| Emor Health Care |
1 |
0.0 |
0.4 |
| emory |
1 |
0.0 |
0.4 |
| Emory |
1 |
0.0 |
0.4 |
| Emory (PCP) |
1 |
0.0 |
0.4 |
| Emory Clinic |
1 |
0.0 |
0.4 |
| Emory Clinic. |
1 |
0.0 |
0.4 |
| Emory Clinic/Hospital |
1 |
0.0 |
0.4 |
| Emory hospital transplant center |
1 |
0.0 |
0.4 |
| Emory Midtown |
1 |
0.0 |
0.4 |
| Emory-Neurological unit for Alzheimer’s |
1 |
0.0 |
0.4 |
| Every month (I take Warfarin) |
1 |
0.0 |
0.4 |
| Eye Doctor/Eye sight associate |
1 |
0.0 |
0.4 |
| Facey Med Group |
1 |
0.0 |
0.4 |
| Family Doctor Emilio Cardona 313-841-7575 |
1 |
0.0 |
0.4 |
| Family Health center |
1 |
0.0 |
0.4 |
| Family practice center |
1 |
0.0 |
0.4 |
| Family practice. |
1 |
0.0 |
0.4 |
| Fern Cave Medical Center |
1 |
0.0 |
0.4 |
| For kidney stone |
1 |
0.0 |
0.4 |
| FPT, Jacksonville, FL |
1 |
0.0 |
0.4 |
| Grady |
1 |
0.0 |
0.4 |
| Grady Health System. |
1 |
0.0 |
0.4 |
| Grady Hospital HTI. |
1 |
0.0 |
0.4 |
| Grady Memorial Purple Pod |
1 |
0.0 |
0.4 |
| Harper Professional. |
1 |
0.0 |
0.4 |
| Harris Health Clinics |
1 |
0.0 |
0.4 |
| Heart doctor |
1 |
0.0 |
0.4 |
| Henry Ford Clinic |
1 |
0.0 |
0.4 |
| Henry Ford Health System |
1 |
0.0 |
0.4 |
| Henry Ford hospital clinic |
1 |
0.0 |
0.4 |
| Henry Ford Hospital system Detroit Mi |
1 |
0.0 |
0.4 |
| Henry Ford Satellite |
1 |
0.0 |
0.4 |
| Home. |
1 |
0.0 |
0.4 |
| Hospital, community HC |
1 |
0.0 |
0.4 |
| Hospital. |
1 |
0.0 |
0.4 |
| Hunt Medical Center. |
1 |
0.0 |
0.4 |
| HWY 138 Riverdale GA 30296 |
1 |
0.0 |
0.4 |
| I go to Kaiser hospital clinic, dedicated doctor. |
1 |
0.0 |
0.4 |
| I haven’t been checked over one year. |
1 |
0.0 |
0.4 |
| I’m retired from Army |
1 |
0.0 |
0.4 |
| In my doctors office |
1 |
0.0 |
0.4 |
| JBACC |
1 |
0.0 |
0.4 |
| Jen-care clinic |
1 |
0.0 |
0.4 |
| Jersey City Medical Center |
1 |
0.0 |
0.4 |
| Just switched to VA |
1 |
0.0 |
0.4 |
| Kaiser |
9 |
0.2 |
3.6 |
| Kaiser —- |
1 |
0.0 |
0.4 |
| Kaiser hospital |
1 |
0.0 |
0.4 |
| Kaiser Hospital |
1 |
0.0 |
0.4 |
| Kaiser Permanente |
1 |
0.0 |
0.4 |
| Kaiser Permanente, Fontana, CA. |
1 |
0.0 |
0.4 |
| Kaiser Permanente. |
1 |
0.0 |
0.4 |
| Kaiser Permanently |
1 |
0.0 |
0.4 |
| Kaiser. |
2 |
0.0 |
0.8 |
| Kaizer, San Francisco |
1 |
0.0 |
0.4 |
| Karmanos |
1 |
0.0 |
0.4 |
| Karmanos Cancer Institute |
1 |
0.0 |
0.4 |
| Karmanos 4100 John |
1 |
0.0 |
0.4 |
| Kaufman Clinic |
1 |
0.0 |
0.4 |
| Keck East Los Angeles |
1 |
0.0 |
0.4 |
| Kelsey |
1 |
0.0 |
0.4 |
| Kelsey Seybold Clinic |
1 |
0.0 |
0.4 |
| Kelsey Seybold. |
1 |
0.0 |
0.4 |
| Kirkwood Family Medicine |
1 |
0.0 |
0.4 |
| LA Care |
1 |
0.0 |
0.4 |
| LA care/Martin Luther King |
1 |
0.0 |
0.4 |
| LA Pain spe |
1 |
0.0 |
0.4 |
| Lab quest PSA levels every three months. |
1 |
0.0 |
0.4 |
| LGBTQ Center |
1 |
0.0 |
0.4 |
| Loma Linda Hospital |
1 |
0.0 |
0.4 |
| LSU Hospital |
1 |
0.0 |
0.4 |
| Medicaid |
1 |
0.0 |
0.4 |
| Medical cancer. |
1 |
0.0 |
0.4 |
| Medical center |
1 |
0.0 |
0.4 |
| Medicare |
1 |
0.0 |
0.4 |
| Medicare and medical —- |
1 |
0.0 |
0.4 |
| Medicate, Pulmonary doctor and urologist in my area |
1 |
0.0 |
0.4 |
| Memorial Hermann Private doctor |
1 |
0.0 |
0.4 |
| Mercer Medicine LLC. |
1 |
0.0 |
0.4 |
| Methodist Hospital |
1 |
0.0 |
0.4 |
| Military facility |
1 |
0.0 |
0.4 |
| Military Hospital |
1 |
0.0 |
0.4 |
| Morehouse/Grady |
1 |
0.0 |
0.4 |
| Moss Memorial Primary Care Clinic |
1 |
0.0 |
0.4 |
| MPTF (UCLA) |
1 |
0.0 |
0.4 |
| My doctor |
1 |
0.0 |
0.4 |
| My doctor, Pablo Splenser. |
1 |
0.0 |
0.4 |
| My regular doctor |
1 |
0.0 |
0.4 |
| Nephrologist |
1 |
0.0 |
0.4 |
| Neurologic |
1 |
0.0 |
0.4 |
| No. |
2 |
0.0 |
0.8 |
| None. |
1 |
0.0 |
0.4 |
| Not HMO |
1 |
0.0 |
0.4 |
| Oak Tree Health |
1 |
0.0 |
0.4 |
| Oakhurst medical center |
1 |
0.0 |
0.4 |
| Ochiner |
1 |
0.0 |
0.4 |
| Ochsner Baptist NOLA. |
1 |
0.0 |
0.4 |
| Ochsner Clinic |
1 |
0.0 |
0.4 |
| Ochsner of New Orleans |
1 |
0.0 |
0.4 |
| Ocshner Clinic |
1 |
0.0 |
0.4 |
| Ocshner Clinic/West Bank. |
1 |
0.0 |
0.4 |
| Open health care clinic |
1 |
0.0 |
0.4 |
| Otero County Regional Medical Center |
1 |
0.0 |
0.4 |
| Palvillon Houston Health Care |
1 |
0.0 |
0.4 |
| Park Plaza Hosp. 1313 Herman Dr. 77004 713-527-5000 |
1 |
0.0 |
0.4 |
| Parkland Memorial Hospital |
1 |
0.0 |
0.4 |
| Parkland. |
1 |
0.0 |
0.4 |
| PCP office. |
1 |
0.0 |
0.4 |
| PCP, Dr. Vannoy |
1 |
0.0 |
0.4 |
| Primary Care |
1 |
0.0 |
0.4 |
| Primary Care / Anual checkup |
1 |
0.0 |
0.4 |
| Primary care doctor |
1 |
0.0 |
0.4 |
| Primary Care Doctor |
1 |
0.0 |
0.4 |
| PRIMARY CARE DOCTOR |
1 |
0.0 |
0.4 |
| Primary care doctor. |
2 |
0.0 |
0.8 |
| Primary care physician |
1 |
0.0 |
0.4 |
| Primary care-Dr. Williams |
1 |
0.0 |
0.4 |
| Primary Doc |
1 |
0.0 |
0.4 |
| Primary doctor |
3 |
0.1 |
1.2 |
| Primary doctor (People Health) |
1 |
0.0 |
0.4 |
| Primary doctor Dr. Zoghbi |
1 |
0.0 |
0.4 |
| primary doctor dr.guagrilia hewitt,nj |
1 |
0.0 |
0.4 |
| Primary doctor thru United Health Care |
1 |
0.0 |
0.4 |
| Primary doctors Rober —- |
1 |
0.0 |
0.4 |
| Primary Michelle Cooke |
1 |
0.0 |
0.4 |
| Primary physician. |
1 |
0.0 |
0.4 |
| Private civilian Doctor too including the VA clinic |
1 |
0.0 |
0.4 |
| Private clinics |
1 |
0.0 |
0.4 |
| Private doctor |
1 |
0.0 |
0.4 |
| Private Doctor |
1 |
0.0 |
0.4 |
| private doctor and the Harper professional building and Sinai-Grace hospital |
1 |
0.0 |
0.4 |
| Private doctor is |
1 |
0.0 |
0.4 |
| Private doctors office |
1 |
0.0 |
0.4 |
| Private doctors office 2200 Emery — Hill. |
1 |
0.0 |
0.4 |
| Private Dr. |
1 |
0.0 |
0.4 |
| Private Dr’s office |
1 |
0.0 |
0.4 |
| Prompt care |
1 |
0.0 |
0.4 |
| Reg. Dr. follow up |
1 |
0.0 |
0.4 |
| Rockdale Family Practice |
1 |
0.0 |
0.4 |
| SAG/AFTRA UCLA TOLUCA LAKE MEDICAL FACILITY |
1 |
0.0 |
0.4 |
| Santa Clara Family Health Plan. |
1 |
0.0 |
0.4 |
| Scan. |
1 |
0.0 |
0.4 |
| Scan/UCLA. |
1 |
0.0 |
0.4 |
| Sini Grace Professional Bldg |
1 |
0.0 |
0.4 |
| Southwell Clinic Hwy 41 N, Tifton, GA 31794 |
1 |
0.0 |
0.4 |
| Southwell Medical Clinic |
1 |
0.0 |
0.4 |
| Specialist Care-UTSW Med. Ctr Dallas |
1 |
0.0 |
0.4 |
| St. Mary’s Livonia |
1 |
0.0 |
0.4 |
| Stanford Health Care-employer benefits |
1 |
0.0 |
0.4 |
| Stanford hospital Palo Alto |
1 |
0.0 |
0.4 |
| Stopped going, mistreated |
1 |
0.0 |
0.4 |
| The —- Plan |
1 |
0.0 |
0.4 |
| The Healing Point Acupuncture and Wellness LLC |
1 |
0.0 |
0.4 |
| The Kauffman Clinic at Emory |
1 |
0.0 |
0.4 |
| Thibodand |
1 |
0.0 |
0.4 |
| Tricity health care |
1 |
0.0 |
0.4 |
| Tulane Hosp. |
1 |
0.0 |
0.4 |
| UCD Medical office |
1 |
0.0 |
0.4 |
| UCLA Health |
1 |
0.0 |
0.4 |
| UCLA Hospital |
1 |
0.0 |
0.4 |
| UCLA Medical Center. |
1 |
0.0 |
0.4 |
| UCSD MEDICAL CENTER |
1 |
0.0 |
0.4 |
| UCSF Medical Center. |
1 |
0.0 |
0.4 |
| UM Ann Arbor |
1 |
0.0 |
0.4 |
| University of Toledo Medical Center |
1 |
0.0 |
0.4 |
| Urgent Car-UT Robert B. Green. |
1 |
0.0 |
0.4 |
| urgent care |
1 |
0.0 |
0.4 |
| Urgent care |
1 |
0.0 |
0.4 |
| Urgent care at VA facility. |
1 |
0.0 |
0.4 |
| Urgent care facility |
1 |
0.0 |
0.4 |
| Urgent Care. |
1 |
0.0 |
0.4 |
| Urologist |
1 |
0.0 |
0.4 |
| Urologist specialist |
1 |
0.0 |
0.4 |
| Urologist/Diabetic Center/Hypertension doctor |
1 |
0.0 |
0.4 |
| Urology Specialist Clinic |
1 |
0.0 |
0.4 |
| UTHSC Da., TX. |
1 |
0.0 |
0.4 |
| UTSW Medical Center, Dallas, TX. Baylor-Scott White-Dallas, TX. Methodist Medical Center, Dallas, TX, Beckely Ave.. |
1 |
0.0 |
0.4 |
| Wayne State Urology |
1 |
0.0 |
0.4 |
| Wellness clinic-Ochsner |
1 |
0.0 |
0.4 |
| Wellstar Medical Center |
1 |
0.0 |
0.4 |
| Wellstar/Northside. |
1 |
0.0 |
0.4 |
| West Georgia Medical Center Villa —- GA |
1 |
0.0 |
0.4 |
| Willis Knighton —- Dr. Jennifer Kelly |
1 |
0.0 |
0.4 |
| Winn Army Community Hospital |
1 |
0.0 |
0.4 |
| Yearly physical exams |
1 |
0.0 |
0.4 |
| NA |
5590 |
95.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
C1: Years lived at current address
- C1. How many years have you lived in your current address?
- 1=Less than 1 year
- 2=1-5 years
- 3=6-10 years
- 4=11-15 years
- 5=16-20 years
- 6=21+ years
c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
levels(c1) <- list(Less_than_1_year="1",
years_1_5="2",
years_6_10="3",
years_11_15="4",
years_16_20="5",
years_21_more="6")
c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
new.d <- data.frame(new.d, c1)
new.d <- apply_labels(new.d, c1 = "living period")
temp.d <- data.frame (new.d, c1)
result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l')
| Less_than_1_year |
198 |
3.4 |
3.4 |
3.4 |
3.4 |
| years_1_5 |
947 |
16.2 |
16.4 |
19.6 |
19.9 |
| years_6_10 |
797 |
13.6 |
13.8 |
33.3 |
33.7 |
| years_11_15 |
726 |
12.4 |
12.6 |
45.7 |
46.3 |
| years_16_20 |
768 |
13.2 |
13.3 |
58.8 |
59.7 |
| years_21_more |
2321 |
39.7 |
40.3 |
98.6 |
100.0 |
| NA |
82 |
1.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C2A: Feel safe walking in the neighborhood
- On average, I felt/feel safe walking in my neighborhood day or night.
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis)
- Childhood or young adult life (up to age 30)
- 1=Strongly Agree
- 2=Agree
- 3=Neutral (neither agree nor disagree)
- 4=Disagree
- 5=Strongly Disagree
c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
levels(c2a1) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2a1)
new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
temp.d <- data.frame (new.d, c2a1)
c2a2 <- as.factor(d[,"c2a2"])
# Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
levels(c2a2) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2a2)
new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
temp.d <- data.frame (new.d, c2a2)
c2a3 <- as.factor(d[,"c2a3"])
# Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
levels(c2a3) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2a3)
new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
temp.d <- data.frame (new.d, c2a3)
result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Strongly_Agree |
2725 |
46.7 |
47.7 |
46.7 |
47.7 |
| Agree |
1941 |
33.2 |
33.9 |
79.9 |
81.6 |
| Neutral |
685 |
11.7 |
12.0 |
91.6 |
93.6 |
| Disagree |
282 |
4.8 |
4.9 |
96.5 |
98.5 |
| Strongly_Disagree |
85 |
1.5 |
1.5 |
97.9 |
100.0 |
| NA |
121 |
2.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
| Strongly_Agree |
2317 |
39.7 |
42.3 |
39.7 |
42.3 |
| Agree |
1970 |
33.7 |
35.9 |
73.4 |
78.2 |
| Neutral |
834 |
14.3 |
15.2 |
87.7 |
93.4 |
| Disagree |
276 |
4.7 |
5.0 |
92.4 |
98.5 |
| Strongly_Disagree |
84 |
1.4 |
1.5 |
93.9 |
100.0 |
| NA |
358 |
6.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Strongly_Agree |
2422 |
41.5 |
44.7 |
41.5 |
44.7 |
| Agree |
1698 |
29.1 |
31.3 |
70.6 |
76.0 |
| Neutral |
838 |
14.4 |
15.5 |
84.9 |
91.5 |
| Disagree |
348 |
6.0 |
6.4 |
90.9 |
97.9 |
| Strongly_Disagree |
114 |
2.0 |
2.1 |
92.8 |
100.0 |
| NA |
419 |
7.2 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C2B: Violence
- Violence was/is not a problem in my neighborhood.
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis)
- Childhood or young adult life (up to age 30)
- 1=Strongly Agree
- 2=Agree
- 3=Neutral (neither agree nor disagree)
- 4=Disagree
- 5=Strongly Disagree
c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
levels(c2b1) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2b1)
new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
temp.d <- data.frame (new.d, c2b1)
c2b2 <- as.factor(d[,"c2b2"])
# Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
levels(c2b2) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2b2)
new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
temp.d <- data.frame (new.d, c2b2)
c2b3 <- as.factor(d[,"c2b3"])
# Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
levels(c2b3) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2b3)
new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
temp.d <- data.frame (new.d, c2b3)
result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Strongly_Agree |
2336 |
40.0 |
41.1 |
40.0 |
41.1 |
| Agree |
1807 |
30.9 |
31.8 |
71.0 |
73.0 |
| Neutral |
841 |
14.4 |
14.8 |
85.4 |
87.8 |
| Disagree |
485 |
8.3 |
8.5 |
93.7 |
96.3 |
| Strongly_Disagree |
208 |
3.6 |
3.7 |
97.2 |
100.0 |
| NA |
162 |
2.8 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
| Strongly_Agree |
1881 |
32.2 |
34.6 |
32.2 |
34.6 |
| Agree |
1858 |
31.8 |
34.1 |
64.0 |
68.7 |
| Neutral |
1011 |
17.3 |
18.6 |
81.3 |
87.3 |
| Disagree |
511 |
8.8 |
9.4 |
90.1 |
96.6 |
| Strongly_Disagree |
183 |
3.1 |
3.4 |
93.2 |
100.0 |
| NA |
395 |
6.8 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Strongly_Agree |
1854 |
31.8 |
34.3 |
31.8 |
34.3 |
| Agree |
1689 |
28.9 |
31.2 |
60.7 |
65.5 |
| Neutral |
1018 |
17.4 |
18.8 |
78.1 |
84.4 |
| Disagree |
614 |
10.5 |
11.4 |
88.6 |
95.7 |
| Strongly_Disagree |
231 |
4.0 |
4.3 |
92.6 |
100.0 |
| NA |
433 |
7.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C2C: Safe from crime
- My neighborhood was/is safe from crime.
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis)
- Childhood or young adult life (up to age 30)
- 1=Strongly Agree
- 2=Agree
- 3=Neutral (neither agree nor disagree)
- 4=Disagree
- 5=Strongly Disagree
c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
levels(c2c1) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2c1)
new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
temp.d <- data.frame (new.d, c2c1)
c2c2 <- as.factor(d[,"c2c2"])
# Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
levels(c2c2) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2c2)
new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
temp.d <- data.frame (new.d, c2c2)
c2c3 <- as.factor(d[,"c2c3"])
# Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
levels(c2c3) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2c3)
new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
temp.d <- data.frame (new.d, c2c3)
result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Strongly_Agree |
1706 |
29.2 |
30.2 |
29.2 |
30.2 |
| Agree |
1800 |
30.8 |
31.8 |
60.0 |
62.0 |
| Neutral |
1218 |
20.9 |
21.5 |
80.9 |
83.6 |
| Disagree |
729 |
12.5 |
12.9 |
93.4 |
96.4 |
| Strongly_Disagree |
201 |
3.4 |
3.6 |
96.8 |
100.0 |
| NA |
185 |
3.2 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
| Strongly_Agree |
1424 |
24.4 |
26.4 |
24.4 |
26.4 |
| Agree |
1828 |
31.3 |
33.9 |
55.7 |
60.2 |
| Neutral |
1257 |
21.5 |
23.3 |
77.2 |
83.5 |
| Disagree |
722 |
12.4 |
13.4 |
89.6 |
96.9 |
| Strongly_Disagree |
168 |
2.9 |
3.1 |
92.5 |
100.0 |
| NA |
440 |
7.5 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Strongly_Agree |
1508 |
25.8 |
28.0 |
25.8 |
28.0 |
| Agree |
1636 |
28.0 |
30.4 |
53.8 |
58.4 |
| Neutral |
1250 |
21.4 |
23.2 |
75.3 |
81.7 |
| Disagree |
743 |
12.7 |
13.8 |
88.0 |
95.5 |
| Strongly_Disagree |
243 |
4.2 |
4.5 |
92.1 |
100.0 |
| NA |
459 |
7.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C3A: Traffic
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- Traffic
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
levels(c3a1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3a1)
new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
temp.d <- data.frame (new.d, c3a1)
c3a2 <- as.factor(d[,"c3a2"])
# Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
levels(c3a2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3a2)
new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
temp.d <- data.frame (new.d, c3a2)
c3a3 <- as.factor(d[,"c3a3"])
# Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
levels(c3a3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3a3)
new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
temp.d <- data.frame (new.d, c3a3)
result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
4029 |
69.0 |
70.8 |
69.0 |
70.8 |
| Somewhat_serious |
1049 |
18.0 |
18.4 |
87.0 |
89.2 |
| Very_serious |
383 |
6.6 |
6.7 |
93.5 |
95.9 |
| Dont_know |
232 |
4.0 |
4.1 |
97.5 |
100.0 |
| NA |
146 |
2.5 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
3805 |
65.2 |
69.1 |
65.2 |
69.1 |
| Somewhat_serious |
1178 |
20.2 |
21.4 |
85.3 |
90.5 |
| Very_serious |
238 |
4.1 |
4.3 |
89.4 |
94.8 |
| Dont_know |
288 |
4.9 |
5.2 |
94.3 |
100.0 |
| NA |
330 |
5.7 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
4194 |
71.8 |
76.7 |
71.8 |
76.7 |
| Somewhat_serious |
689 |
11.8 |
12.6 |
83.6 |
89.3 |
| Very_serious |
157 |
2.7 |
2.9 |
86.3 |
92.2 |
| Dont_know |
427 |
7.3 |
7.8 |
93.6 |
100.0 |
| NA |
372 |
6.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C3B: Noise
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- A lot of noise
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
levels(c3b1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3b1)
new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
temp.d <- data.frame (new.d, c3b1)
c3b2 <- as.factor(d[,"c3b2"])
# Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
levels(c3b2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3b2)
new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
temp.d <- data.frame (new.d, c3b2)
c3b3 <- as.factor(d[,"c3b3"])
# Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
levels(c3b3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3b3)
new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
temp.d <- data.frame (new.d, c3b3)
result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
4637 |
79.4 |
82.0 |
79.4 |
82.0 |
| Somewhat_serious |
731 |
12.5 |
12.9 |
91.9 |
94.9 |
| Very_serious |
159 |
2.7 |
2.8 |
94.7 |
97.7 |
| Dont_know |
130 |
2.2 |
2.3 |
96.9 |
100.0 |
| NA |
182 |
3.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
4217 |
72.2 |
77.2 |
72.2 |
77.2 |
| Somewhat_serious |
882 |
15.1 |
16.1 |
87.3 |
93.3 |
| Very_serious |
169 |
2.9 |
3.1 |
90.2 |
96.4 |
| Dont_know |
195 |
3.3 |
3.6 |
93.6 |
100.0 |
| NA |
376 |
6.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
4185 |
71.7 |
77.0 |
71.7 |
77.0 |
| Somewhat_serious |
741 |
12.7 |
13.6 |
84.4 |
90.7 |
| Very_serious |
181 |
3.1 |
3.3 |
87.5 |
94.0 |
| Dont_know |
325 |
5.6 |
6.0 |
93.0 |
100.0 |
| NA |
407 |
7.0 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C3C: Trash and litter
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- Trash and litter
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
levels(c3c1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3c1)
new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
temp.d <- data.frame (new.d, c3c1)
c3c2 <- as.factor(d[,"c3c2"])
# Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
levels(c3c2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3c2)
new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
temp.d <- data.frame (new.d, c3c2)
c3c3 <- as.factor(d[,"c3c3"])
# Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
levels(c3c3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3c3)
new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
temp.d <- data.frame (new.d, c3c3)
result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
4690 |
80.3 |
83.0 |
80.3 |
83.0 |
| Somewhat_serious |
612 |
10.5 |
10.8 |
90.8 |
93.8 |
| Very_serious |
245 |
4.2 |
4.3 |
95.0 |
98.1 |
| Dont_know |
107 |
1.8 |
1.9 |
96.8 |
100.0 |
| NA |
185 |
3.2 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
4359 |
74.7 |
79.8 |
74.7 |
79.8 |
| Somewhat_serious |
754 |
12.9 |
13.8 |
87.6 |
93.6 |
| Very_serious |
178 |
3.0 |
3.3 |
90.6 |
96.9 |
| Dont_know |
172 |
2.9 |
3.1 |
93.6 |
100.0 |
| NA |
376 |
6.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
4223 |
72.3 |
77.7 |
72.3 |
77.7 |
| Somewhat_serious |
726 |
12.4 |
13.4 |
84.8 |
91.1 |
| Very_serious |
207 |
3.5 |
3.8 |
88.3 |
94.9 |
| Dont_know |
278 |
4.8 |
5.1 |
93.1 |
100.0 |
| NA |
405 |
6.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C3D: Too much light at night
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- Too much light at night
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
levels(c3d1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3d1)
new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
temp.d <- data.frame (new.d, c3d1)
c3d2 <- as.factor(d[,"c3d2"])
# Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
levels(c3d2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3d2)
new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
temp.d <- data.frame (new.d, c3d2)
c3d3 <- as.factor(d[,"c3d3"])
# Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
levels(c3d3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3d3)
new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
temp.d <- data.frame (new.d, c3d3)
result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
5208 |
89.2 |
92.2 |
89.2 |
92.2 |
| Somewhat_serious |
181 |
3.1 |
3.2 |
92.3 |
95.4 |
| Very_serious |
49 |
0.8 |
0.9 |
93.1 |
96.3 |
| Dont_know |
208 |
3.6 |
3.7 |
96.7 |
100.0 |
| NA |
193 |
3.3 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
4880 |
83.6 |
89.5 |
83.6 |
89.5 |
| Somewhat_serious |
280 |
4.8 |
5.1 |
88.4 |
94.6 |
| Very_serious |
48 |
0.8 |
0.9 |
89.2 |
95.5 |
| Dont_know |
245 |
4.2 |
4.5 |
93.4 |
100.0 |
| NA |
386 |
6.6 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
4750 |
81.3 |
87.3 |
81.3 |
87.3 |
| Somewhat_serious |
252 |
4.3 |
4.6 |
85.7 |
92.0 |
| Very_serious |
53 |
0.9 |
1.0 |
86.6 |
92.9 |
| Dont_know |
384 |
6.6 |
7.1 |
93.1 |
100.0 |
| NA |
400 |
6.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C4A: Neighbors talking outside
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
levels(c4a1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4a1)
new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
temp.d <- data.frame (new.d, c4a1)
c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA"
levels(c4a2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4a2)
new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
temp.d <- data.frame (new.d, c4a2)
c4a3 <- as.factor(d[,"c4a3"])
# Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
levels(c4a3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4a3)
new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
temp.d <- data.frame (new.d, c4a3)
result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
2131 |
36.5 |
37.3 |
36.5 |
37.3 |
| Sometimes |
2391 |
40.9 |
41.9 |
77.4 |
79.2 |
| Rarely_Never |
1095 |
18.8 |
19.2 |
96.2 |
98.4 |
| Dont_know |
92 |
1.6 |
1.6 |
97.8 |
100.0 |
| NA |
130 |
2.2 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
2125 |
36.4 |
38.6 |
36.4 |
38.6 |
| Sometimes |
2422 |
41.5 |
43.9 |
77.9 |
82.5 |
| Rarely_Never |
776 |
13.3 |
14.1 |
91.2 |
96.6 |
| Dont_know |
188 |
3.2 |
3.4 |
94.4 |
100.0 |
| NA |
328 |
5.6 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
3132 |
53.6 |
57.4 |
53.6 |
57.4 |
| Sometimes |
1508 |
25.8 |
27.6 |
79.5 |
85.0 |
| Rarely_Never |
504 |
8.6 |
9.2 |
88.1 |
94.2 |
| Dont_know |
316 |
5.4 |
5.8 |
93.5 |
100.0 |
| NA |
379 |
6.5 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C4B: Neighbors watch out for each other
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How often do/did neighbors watch out for each other, such as calling if they see a problem?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
levels(c4b1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4b1)
new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
temp.d <- data.frame (new.d, c4b1)
c4b2 <- as.factor(d[,"c4b2"])
# Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
levels(c4b2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4b2)
new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
temp.d <- data.frame (new.d, c4b2)
c4b3 <- as.factor(d[,"c4b3"])
# Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
levels(c4b3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4b3)
new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
temp.d <- data.frame (new.d, c4b3)
result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
2622 |
44.9 |
46.6 |
44.9 |
46.6 |
| Sometimes |
1770 |
30.3 |
31.5 |
75.2 |
78.1 |
| Rarely_Never |
868 |
14.9 |
15.4 |
90.1 |
93.6 |
| Dont_know |
362 |
6.2 |
6.4 |
96.3 |
100.0 |
| NA |
217 |
3.7 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
2268 |
38.8 |
41.8 |
38.8 |
41.8 |
| Sometimes |
1984 |
34.0 |
36.6 |
72.8 |
78.4 |
| Rarely_Never |
816 |
14.0 |
15.0 |
86.8 |
93.5 |
| Dont_know |
354 |
6.1 |
6.5 |
92.9 |
100.0 |
| NA |
417 |
7.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
3119 |
53.4 |
58.0 |
53.4 |
58.0 |
| Sometimes |
1272 |
21.8 |
23.6 |
75.2 |
81.6 |
| Rarely_Never |
522 |
8.9 |
9.7 |
84.1 |
91.3 |
| Dont_know |
466 |
8.0 |
8.7 |
92.1 |
100.0 |
| NA |
460 |
7.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C4C: Neighbors know by name
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How many neighbors do/did you know by name?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
levels(c4c1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4c1)
new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
temp.d <- data.frame (new.d, c4c1)
c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
levels(c4c2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4c2)
new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
temp.d <- data.frame (new.d, c4c2)
c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
levels(c4c3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4c3)
new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
temp.d <- data.frame (new.d, c4c3)
result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
1630 |
27.9 |
29.2 |
27.9 |
29.2 |
| Sometimes |
2338 |
40.0 |
41.8 |
68.0 |
71.0 |
| Rarely_Never |
1533 |
26.3 |
27.4 |
94.2 |
98.4 |
| Dont_know |
87 |
1.5 |
1.6 |
95.7 |
100.0 |
| NA |
251 |
4.3 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
1709 |
29.3 |
31.9 |
29.3 |
31.9 |
| Sometimes |
2376 |
40.7 |
44.3 |
70.0 |
76.2 |
| Rarely_Never |
1133 |
19.4 |
21.1 |
89.4 |
97.3 |
| Dont_know |
146 |
2.5 |
2.7 |
91.9 |
100.0 |
| NA |
475 |
8.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
3198 |
54.8 |
60.1 |
54.8 |
60.1 |
| Sometimes |
1294 |
22.2 |
24.3 |
76.9 |
84.4 |
| Rarely_Never |
596 |
10.2 |
11.2 |
87.1 |
95.5 |
| Dont_know |
237 |
4.1 |
4.5 |
91.2 |
100.0 |
| NA |
514 |
8.8 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C4D: Friendly talks with neighbors
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How many neighbors do/did you have a friendly talk with at least once a week?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
levels(c4d1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4d1)
new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
temp.d <- data.frame (new.d, c4d1)
c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
levels(c4d2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4d2)
new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
temp.d <- data.frame (new.d, c4d2)
c4d3 <- as.factor(d[,"c4d3"])
# Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
levels(c4d3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4d3)
new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
temp.d <- data.frame (new.d, c4d3)
result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
759 |
13.0 |
13.5 |
13.0 |
13.5 |
| Sometimes |
2095 |
35.9 |
37.3 |
48.9 |
50.8 |
| Rarely_Never |
2656 |
45.5 |
47.3 |
94.4 |
98.2 |
| Dont_know |
103 |
1.8 |
1.8 |
96.1 |
100.0 |
| NA |
226 |
3.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
939 |
16.1 |
17.4 |
16.1 |
17.4 |
| Sometimes |
2387 |
40.9 |
44.2 |
57.0 |
61.6 |
| Rarely_Never |
1895 |
32.5 |
35.1 |
89.4 |
96.7 |
| Dont_know |
176 |
3.0 |
3.3 |
92.4 |
100.0 |
| NA |
442 |
7.6 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
2310 |
39.6 |
43.0 |
39.6 |
43.0 |
| Sometimes |
1681 |
28.8 |
31.3 |
68.4 |
74.3 |
| Rarely_Never |
1071 |
18.3 |
19.9 |
86.7 |
94.2 |
| Dont_know |
309 |
5.3 |
5.8 |
92.0 |
100.0 |
| NA |
468 |
8.0 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
C4E: Ask neighbors for help
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
levels(c4e1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4e1)
new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
temp.d <- data.frame (new.d, c4e1)
c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
levels(c4e2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4e2)
new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
temp.d <- data.frame (new.d, c4e2)
c4e3 <- as.factor(d[,"c4e3"])
# Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
levels(c4e3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4e3)
new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
temp.d <- data.frame (new.d, c4e3)
result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
926 |
15.9 |
17.0 |
15.9 |
17.0 |
| Sometimes |
1854 |
31.8 |
34.0 |
47.6 |
50.9 |
| Rarely_Never |
2189 |
37.5 |
40.1 |
85.1 |
91.1 |
| Dont_know |
488 |
8.4 |
8.9 |
93.5 |
100.0 |
| NA |
382 |
6.5 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
993 |
17.0 |
18.9 |
17.0 |
18.9 |
| Sometimes |
1979 |
33.9 |
37.6 |
50.9 |
56.5 |
| Rarely_Never |
1860 |
31.9 |
35.4 |
82.8 |
91.9 |
| Dont_know |
428 |
7.3 |
8.1 |
90.1 |
100.0 |
| NA |
579 |
9.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
2074 |
35.5 |
39.5 |
35.5 |
39.5 |
| Sometimes |
1585 |
27.1 |
30.2 |
62.7 |
69.7 |
| Rarely_Never |
1099 |
18.8 |
20.9 |
81.5 |
90.7 |
| Dont_know |
489 |
8.4 |
9.3 |
89.9 |
100.0 |
| NA |
592 |
10.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
D1: Treat you because of your race/ethnicity
- D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
- At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
- For unfair reasons, have you ever not been hired for a job?
- Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
- Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
- Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
- Have you ever been unfairly denied a bank loan?
- Have you ever been unfairly treated when getting medical care?
- If yes, How stressful was this experience?
- 1=Not at all
- 2=A little
- 3=Somewhat
- 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
levels(d1aa) <- list(No="1",
Yes="2")
d1aa <- ordered(d1aa, c("No","Yes"))
new.d <- data.frame(new.d, d1aa)
new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
temp.d <- data.frame (new.d, d1aa)
d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA"
levels(d1ab) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1ab <- ordered(d1ab, c("No","Yes"))
new.d <- data.frame(new.d, d1ab)
new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
temp.d <- data.frame (new.d, d1ab)
result<-questionr::freq(temp.d$d1aa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
| No |
2898 |
49.6 |
51.4 |
| Yes |
2737 |
46.9 |
48.6 |
| NA |
204 |
3.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
# b. For unfair reasons, have you ever not been hired for a job?
d1ba <- as.factor(d[,"d1ba"])
# Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
levels(d1ba) <- list(No="1",
Yes="2")
d1ba <- ordered(d1ba, c("No","Yes"))
new.d <- data.frame(new.d, d1ba)
new.d <- apply_labels(new.d, d1ba = "not be hired")
temp.d <- data.frame (new.d, d1ba)
d1bb <- as.factor(d[,"d1bb"])
# Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
levels(d1bb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1bb <- ordered(d1bb, c("No","Yes"))
new.d <- data.frame(new.d, d1bb)
new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
temp.d <- data.frame (new.d, d1bb)
result<-questionr::freq(temp.d$d1ba,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
| No |
3393 |
58.1 |
61.2 |
| Yes |
2153 |
36.9 |
38.8 |
| NA |
293 |
5.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
d1ca <- as.factor(d[,"d1ca"])
# Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
levels(d1ca) <- list(No="1",
Yes="2")
d1ca <- ordered(d1ca, c( "No","Yes"))
new.d <- data.frame(new.d, d1ca)
new.d <- apply_labels(new.d, d1ca = "By police")
temp.d <- data.frame (new.d, d1ca)
result<-questionr::freq(temp.d$d1ca,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
| No |
2690 |
46.1 |
47.8 |
| Yes |
2943 |
50.4 |
52.2 |
| NA |
206 |
3.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
d1cb <- as.factor(d[,"d1cb"])
# Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
levels(d1cb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1cb <- ordered(d1cb, c("No","Yes"))
new.d <- data.frame(new.d, d1cb)
new.d <- apply_labels(new.d, d1cb = "By police-stressful")
temp.d <- data.frame (new.d, d1cb)
result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
d1da <- as.factor(d[,"d1da"])
# Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
levels(d1da) <- list(No="1",
Yes="2")
d1da <- ordered(d1da, c( "No","Yes"))
new.d <- data.frame(new.d, d1da)
new.d <- apply_labels(new.d, d1da = "unfair education")
temp.d <- data.frame (new.d, d1da)
result<-questionr::freq(temp.d$d1da,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
| No |
4413 |
75.6 |
78.5 |
| Yes |
1211 |
20.7 |
21.5 |
| NA |
215 |
3.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
d1db <- as.factor(d[,"d1db"])
# Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
levels(d1db) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1db <- ordered(d1db, c("No","Yes"))
new.d <- data.frame(new.d, d1db)
new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
temp.d <- data.frame (new.d, d1db)
result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
d1ea <- as.factor(d[,"d1ea"])
# Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
levels(d1ea) <- list(No="1",
Yes="2")
d1ea <- ordered(d1ea, c("No","Yes"))
new.d <- data.frame(new.d, d1ea)
new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
temp.d <- data.frame (new.d, d1ea)
result<-questionr::freq(temp.d$d1ea,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
| No |
4655 |
79.7 |
82.3 |
| Yes |
1002 |
17.2 |
17.7 |
| NA |
182 |
3.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
d1eb <- as.factor(d[,"d1eb"])
# Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
levels(d1eb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1eb <- ordered(d1eb, c("No","Yes"))
new.d <- data.frame(new.d, d1eb)
new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
temp.d <- data.frame (new.d, d1eb)
result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
# f. Have you ever been unfairly denied a bank loan?
d1fa <- as.factor(d[,"d1fa"])
# Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
levels(d1fa) <- list(No="1",
Yes="2")
d1fa <- ordered(d1fa, c("No","Yes"))
new.d <- data.frame(new.d, d1fa)
new.d <- apply_labels(new.d, d1fa = "Bank loan")
temp.d <- data.frame (new.d, d1fa)
result<-questionr::freq(temp.d$d1fa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
| No |
4076 |
69.8 |
72.7 |
| Yes |
1531 |
26.2 |
27.3 |
| NA |
232 |
4.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
d1fb <- as.factor(d[,"d1fb"])
# Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
levels(d1fb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1fb <- ordered(d1fb, c("No","Yes"))
new.d <- data.frame(new.d, d1fb)
new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
temp.d <- data.frame (new.d, d1fb)
result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
# g. Have you ever been unfairly treated when getting medical care?
d1ga <- as.factor(d[,"d1ga"])
# Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
levels(d1ga) <- list(No="1",
Yes="2")
d1ga <- ordered(d1ga, c("No","Yes"))
new.d <- data.frame(new.d, d1ga)
new.d <- apply_labels(new.d, d1ga = "unfair medical care")
temp.d <- data.frame (new.d, d1ga)
result<-questionr::freq(temp.d$d1ga,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
| No |
4710 |
80.7 |
83.8 |
| Yes |
910 |
15.6 |
16.2 |
| NA |
219 |
3.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
d1gb <- as.factor(d[,"d1gb"])
# Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
levels(d1gb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1gb <- ordered(d1gb, c("No","Yes"))
new.d <- data.frame(new.d, d1gb)
new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
temp.d <- data.frame (new.d, d1gb)
result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
| No |
0 |
0 |
NaN |
0 |
NaN |
| Yes |
0 |
0 |
NaN |
0 |
NaN |
| NA |
5839 |
100 |
NA |
100 |
NA |
| Total |
5839 |
100 |
100 |
100 |
100 |
D2: Medical Mistrust
- D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
levels(d2a) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2a)
new.d <- apply_labels(new.d, d2a = "deceived or misled")
temp.d <- data.frame (new.d, d2a)
result<-questionr::freq(temp.d$d2a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
| Strongly_Agree |
802 |
13.7 |
14.4 |
| Somewhat_Agree |
2434 |
41.7 |
43.6 |
| Somewhat_Disagree |
1332 |
22.8 |
23.9 |
| Strongly_Disagree |
1009 |
17.3 |
18.1 |
| NA |
262 |
4.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
levels(d2b) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2b)
new.d <- apply_labels(new.d, d2b = "personal affairs")
temp.d <- data.frame (new.d, d2b)
result<-questionr::freq(temp.d$d2b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
| Strongly_Agree |
711 |
12.2 |
12.7 |
| Somewhat_Agree |
1952 |
33.4 |
34.9 |
| Somewhat_Disagree |
1721 |
29.5 |
30.8 |
| Strongly_Disagree |
1206 |
20.7 |
21.6 |
| NA |
249 |
4.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
levels(d2c) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2c)
new.d <- apply_labels(new.d, d2c = "harmful experiments")
temp.d <- data.frame (new.d, d2c)
result<-questionr::freq(temp.d$d2c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
| Strongly_Agree |
1067 |
18.3 |
19.7 |
| Somewhat_Agree |
1812 |
31.0 |
33.4 |
| Somewhat_Disagree |
1389 |
23.8 |
25.6 |
| Strongly_Disagree |
1159 |
19.8 |
21.4 |
| NA |
412 |
7.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# d. Rich patients receive better care at hospitals than poor patients.
d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
levels(d2d) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2d)
new.d <- apply_labels(new.d, d2d = "Rich patients better care")
temp.d <- data.frame (new.d, d2d)
result<-questionr::freq(temp.d$d2d,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
| Strongly_Agree |
3053 |
52.3 |
54.9 |
| Somewhat_Agree |
1490 |
25.5 |
26.8 |
| Somewhat_Disagree |
559 |
9.6 |
10.0 |
| Strongly_Disagree |
463 |
7.9 |
8.3 |
| NA |
274 |
4.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# e. Male patients receive better care at hospitals than female patients.
d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
levels(d2e) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2e)
new.d <- apply_labels(new.d, d2e = "Male patients better care")
temp.d <- data.frame (new.d, d2e)
result<-questionr::freq(temp.d$d2e,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
| Strongly_Agree |
256 |
4.4 |
4.7 |
| Somewhat_Agree |
869 |
14.9 |
16.0 |
| Somewhat_Disagree |
2356 |
40.3 |
43.4 |
| Strongly_Disagree |
1946 |
33.3 |
35.9 |
| NA |
412 |
7.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3A: Treated with less respect
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been treated with less respect than other people
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
levels(d3a1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3a1)
new.d <- apply_labels(new.d, d3a1 = "less respect-current")
temp.d <- data.frame (new.d, d3a1)
result<-questionr::freq(temp.d$d3a1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
1646 |
28.2 |
28.8 |
| Rarely |
1757 |
30.1 |
30.7 |
| Sometimes |
1975 |
33.8 |
34.5 |
| Often |
339 |
5.8 |
5.9 |
| NA |
122 |
2.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
levels(d3a2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3a2)
new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
temp.d <- data.frame (new.d, d3a2)
result<-questionr::freq(temp.d$d3a2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1266 |
21.7 |
23.3 |
| Rarely |
1605 |
27.5 |
29.5 |
| Sometimes |
2130 |
36.5 |
39.1 |
| Often |
442 |
7.6 |
8.1 |
| NA |
396 |
6.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3a3 <- as.factor(d[,"d3a3"])
# Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
levels(d3a3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3a3)
new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
temp.d <- data.frame (new.d, d3a3)
result<-questionr::freq(temp.d$d3a3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1172 |
20.1 |
21.7 |
| Rarely |
1205 |
20.6 |
22.3 |
| Sometimes |
2070 |
35.5 |
38.4 |
| Often |
947 |
16.2 |
17.6 |
| NA |
445 |
7.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3B: Received poorer service
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have received poorer service than other people at restaurants or stores
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
levels(d3b1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3b1)
new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
temp.d <- data.frame (new.d, d3b1)
result<-questionr::freq(temp.d$d3b1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
1241 |
21.3 |
21.9 |
| Rarely |
1954 |
33.5 |
34.4 |
| Sometimes |
2178 |
37.3 |
38.4 |
| Often |
304 |
5.2 |
5.4 |
| NA |
162 |
2.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3b2 <- as.factor(d[,"d3b2"])
# Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
levels(d3b2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3b2)
new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
temp.d <- data.frame (new.d, d3b2)
result<-questionr::freq(temp.d$d3b2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
970 |
16.6 |
17.9 |
| Rarely |
1686 |
28.9 |
31.1 |
| Sometimes |
2381 |
40.8 |
44.0 |
| Often |
379 |
6.5 |
7.0 |
| NA |
423 |
7.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3b3 <- as.factor(d[,"d3b3"])
# Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
levels(d3b3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3b3)
new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
temp.d <- data.frame (new.d, d3b3)
result<-questionr::freq(temp.d$d3b3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
964 |
16.5 |
18.0 |
| Rarely |
1270 |
21.8 |
23.7 |
| Sometimes |
2240 |
38.4 |
41.8 |
| Often |
884 |
15.1 |
16.5 |
| NA |
481 |
8.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3C: Think you are not smart
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they think you are not smart
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
levels(d3c1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3c1)
new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
temp.d <- data.frame (new.d, d3c1)
result<-questionr::freq(temp.d$d3c1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
1567 |
26.8 |
27.8 |
| Rarely |
1778 |
30.5 |
31.5 |
| Sometimes |
1853 |
31.7 |
32.8 |
| Often |
444 |
7.6 |
7.9 |
| NA |
197 |
3.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
levels(d3c2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3c2)
new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
temp.d <- data.frame (new.d, d3c2)
result<-questionr::freq(temp.d$d3c2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1300 |
22.3 |
24.1 |
| Rarely |
1693 |
29.0 |
31.4 |
| Sometimes |
1936 |
33.2 |
35.9 |
| Often |
461 |
7.9 |
8.6 |
| NA |
449 |
7.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3c3 <- as.factor(d[,"d3c3"])
# Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
levels(d3c3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3c3)
new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
temp.d <- data.frame (new.d, d3c3)
result<-questionr::freq(temp.d$d3c3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1194 |
20.4 |
22.3 |
| Rarely |
1374 |
23.5 |
25.7 |
| Sometimes |
1997 |
34.2 |
37.3 |
| Often |
783 |
13.4 |
14.6 |
| NA |
491 |
8.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3D: Be afraid of you
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they are afraid of you
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
levels(d3d1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3d1)
new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
temp.d <- data.frame (new.d, d3d1)
result<-questionr::freq(temp.d$d3d1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
2051 |
35.1 |
36.2 |
| Rarely |
1572 |
26.9 |
27.7 |
| Sometimes |
1681 |
28.8 |
29.6 |
| Often |
368 |
6.3 |
6.5 |
| NA |
167 |
2.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3d2 <- as.factor(d[,"d3d2"])
# Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
levels(d3d2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3d2)
new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
temp.d <- data.frame (new.d, d3d2)
result<-questionr::freq(temp.d$d3d2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1647 |
28.2 |
30.5 |
| Rarely |
1460 |
25.0 |
27.0 |
| Sometimes |
1812 |
31.0 |
33.5 |
| Often |
489 |
8.4 |
9.0 |
| NA |
431 |
7.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3d3 <- as.factor(d[,"d3d3"])
# Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
levels(d3d3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3d3)
new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
temp.d <- data.frame (new.d, d3d3)
result<-questionr::freq(temp.d$d3d3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1752 |
30.0 |
32.6 |
| Rarely |
1347 |
23.1 |
25.1 |
| Sometimes |
1627 |
27.9 |
30.3 |
| Often |
643 |
11.0 |
12.0 |
| NA |
470 |
8.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3E: Think you are dishonest
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they think you are dishonest
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
levels(d3e1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3e1)
new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
temp.d <- data.frame (new.d, d3e1)
result<-questionr::freq(temp.d$d3e1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
2423 |
41.5 |
42.8 |
| Rarely |
1661 |
28.4 |
29.3 |
| Sometimes |
1289 |
22.1 |
22.8 |
| Often |
292 |
5.0 |
5.2 |
| NA |
174 |
3.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3e2 <- as.factor(d[,"d3e2"])
# Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
levels(d3e2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3e2)
new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
temp.d <- data.frame (new.d, d3e2)
result<-questionr::freq(temp.d$d3e2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1973 |
33.8 |
36.6 |
| Rarely |
1602 |
27.4 |
29.7 |
| Sometimes |
1467 |
25.1 |
27.2 |
| Often |
355 |
6.1 |
6.6 |
| NA |
442 |
7.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3e3 <- as.factor(d[,"d3e3"])
# Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
levels(d3e3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3e3)
new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
temp.d <- data.frame (new.d, d3e3)
result<-questionr::freq(temp.d$d3e3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1894 |
32.4 |
35.3 |
| Rarely |
1376 |
23.6 |
25.6 |
| Sometimes |
1516 |
26.0 |
28.3 |
| Often |
579 |
9.9 |
10.8 |
| NA |
474 |
8.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3F: Better than you
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they’re better than you are
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
levels(d3f1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3f1)
new.d <- apply_labels(new.d, d3f1 = "better than you-current")
temp.d <- data.frame (new.d, d3f1)
result<-questionr::freq(temp.d$d3f1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
898 |
15.4 |
15.8 |
| Rarely |
1564 |
26.8 |
27.6 |
| Sometimes |
2480 |
42.5 |
43.7 |
| Often |
729 |
12.5 |
12.9 |
| NA |
168 |
2.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3f2 <- as.factor(d[,"d3f2"])
# Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
levels(d3f2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3f2)
new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
temp.d <- data.frame (new.d, d3f2)
result<-questionr::freq(temp.d$d3f2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
678 |
11.6 |
12.5 |
| Rarely |
1371 |
23.5 |
25.3 |
| Sometimes |
2593 |
44.4 |
47.9 |
| Often |
776 |
13.3 |
14.3 |
| NA |
421 |
7.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
levels(d3f3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3f3)
new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
temp.d <- data.frame (new.d, d3f3)
result<-questionr::freq(temp.d$d3f3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
679 |
11.6 |
12.6 |
| Rarely |
1101 |
18.9 |
20.5 |
| Sometimes |
2355 |
40.3 |
43.8 |
| Often |
1247 |
21.4 |
23.2 |
| NA |
457 |
7.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3G: Insulted
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been called names or insulted
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
levels(d3g1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3g1)
new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
temp.d <- data.frame (new.d, d3g1)
result<-questionr::freq(temp.d$d3g1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
2066 |
35.4 |
36.5 |
| Rarely |
1975 |
33.8 |
34.9 |
| Sometimes |
1377 |
23.6 |
24.4 |
| Often |
235 |
4.0 |
4.2 |
| NA |
186 |
3.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3g2 <- as.factor(d[,"d3g2"])
# Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
levels(d3g2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3g2)
new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
temp.d <- data.frame (new.d, d3g2)
result<-questionr::freq(temp.d$d3g2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1361 |
23.3 |
25.2 |
| Rarely |
1995 |
34.2 |
36.9 |
| Sometimes |
1759 |
30.1 |
32.5 |
| Often |
291 |
5.0 |
5.4 |
| NA |
433 |
7.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3g3 <- as.factor(d[,"d3g3"])
# Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
levels(d3g3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3g3)
new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
temp.d <- data.frame (new.d, d3g3)
result<-questionr::freq(temp.d$d3g3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
928 |
15.9 |
17.3 |
| Rarely |
1441 |
24.7 |
26.8 |
| Sometimes |
2186 |
37.4 |
40.7 |
| Often |
814 |
13.9 |
15.2 |
| NA |
470 |
8.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3H: Threatened or harassed
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been threatened or harassed
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
levels(d3h1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3h1)
new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
temp.d <- data.frame (new.d, d3h1)
result<-questionr::freq(temp.d$d3h1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
3133 |
53.7 |
55.5 |
| Rarely |
1663 |
28.5 |
29.5 |
| Sometimes |
755 |
12.9 |
13.4 |
| Often |
91 |
1.6 |
1.6 |
| NA |
197 |
3.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3h2 <- as.factor(d[,"d3h2"])
# Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
levels(d3h2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3h2)
new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
temp.d <- data.frame (new.d, d3h2)
result<-questionr::freq(temp.d$d3h2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
2314 |
39.6 |
43.0 |
| Rarely |
1879 |
32.2 |
34.9 |
| Sometimes |
1038 |
17.8 |
19.3 |
| Often |
155 |
2.7 |
2.9 |
| NA |
453 |
7.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3h3 <- as.factor(d[,"d3h3"])
# Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
levels(d3h3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3h3)
new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
temp.d <- data.frame (new.d, d3h3)
result<-questionr::freq(temp.d$d3h3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1786 |
30.6 |
33.4 |
| Rarely |
1582 |
27.1 |
29.6 |
| Sometimes |
1551 |
26.6 |
29.0 |
| Often |
433 |
7.4 |
8.1 |
| NA |
487 |
8.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3I: Followed around in stores
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been followed around in stores
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
levels(d3i1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3i1)
new.d <- apply_labels(new.d, d3i1 = "be followed-current")
temp.d <- data.frame (new.d, d3i1)
result<-questionr::freq(temp.d$d3i1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
2110 |
36.1 |
37.3 |
| Rarely |
1560 |
26.7 |
27.6 |
| Sometimes |
1548 |
26.5 |
27.4 |
| Often |
440 |
7.5 |
7.8 |
| NA |
181 |
3.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3i2 <- as.factor(d[,"d3i2"])
# Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
levels(d3i2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3i2)
new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
temp.d <- data.frame (new.d, d3i2)
result<-questionr::freq(temp.d$d3i2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1476 |
25.3 |
27.3 |
| Rarely |
1450 |
24.8 |
26.8 |
| Sometimes |
1897 |
32.5 |
35.1 |
| Often |
589 |
10.1 |
10.9 |
| NA |
427 |
7.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3i3 <- as.factor(d[,"d3i3"])
# Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
levels(d3i3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3i3)
new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
temp.d <- data.frame (new.d, d3i3)
result<-questionr::freq(temp.d$d3i3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1219 |
20.9 |
22.7 |
| Rarely |
1061 |
18.2 |
19.7 |
| Sometimes |
1936 |
33.2 |
36.0 |
| Often |
1163 |
19.9 |
21.6 |
| NA |
460 |
7.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D3J: How stressful
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
levels(d3j1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3j1)
new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
temp.d <- data.frame (new.d, d3j1)
result<-questionr::freq(temp.d$d3j1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
2411 |
41.3 |
42.7 |
| Rarely |
1893 |
32.4 |
33.6 |
| Sometimes |
1007 |
17.2 |
17.9 |
| Often |
329 |
5.6 |
5.8 |
| NA |
199 |
3.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
d3j2 <- as.factor(d[,"d3j2"])
# Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
levels(d3j2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3j2)
new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
temp.d <- data.frame (new.d, d3j2)
result<-questionr::freq(temp.d$d3j2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
1819 |
31.2 |
33.8 |
| Rarely |
1914 |
32.8 |
35.5 |
| Sometimes |
1256 |
21.5 |
23.3 |
| Often |
398 |
6.8 |
7.4 |
| NA |
452 |
7.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
d3j3 <- as.factor(d[,"d3j3"])
# Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
levels(d3j3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3j3)
new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
temp.d <- data.frame (new.d, d3j3)
result<-questionr::freq(temp.d$d3j3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
1635 |
28.0 |
30.5 |
| Rarely |
1692 |
29.0 |
31.5 |
| Sometimes |
1339 |
22.9 |
25.0 |
| Often |
697 |
11.9 |
13.0 |
| NA |
476 |
8.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
D4: How you currently see yourself
- D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
- You’ve always felt that you could make of your life pretty much what you wanted to make of it.
- Once you make up your mind to do something, you stay with it until the job is completely done.
- You like doing things that other people thought could not be done.
- When things don’t go the way you want them to, that just makes you work even harder.
- Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
- It’s not always easy, but you manage to find a way to do the things you really need to get done.
- Very seldom have you been disappointed by the results of your hard work.
- You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
- In the past, even when things got really tough, you never lost sight of your goals.
- It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
- You don’t let your personal feelings get in the way of doing a job.
- Hard work has really helped you to get ahead in life.
- 1=Strongly Agree
- 2=Somewhat Agree
- 3=Somewhat Disagree
- 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
levels(d4a) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4a)
new.d <- apply_labels(new.d, d4a = "make life")
temp.d <- data.frame (new.d, d4a)
result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
| Strongly_Agree |
2887 |
49.4 |
50.5 |
49.4 |
50.5 |
| Somewhat_Agree |
2279 |
39.0 |
39.8 |
88.5 |
90.3 |
| Somewhat_Disagree |
450 |
7.7 |
7.9 |
96.2 |
98.2 |
| Strongly_Disagree |
105 |
1.8 |
1.8 |
98.0 |
100.0 |
| NA |
118 |
2.0 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
d4b <- as.factor(d[,"d4b"])
# Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
levels(d4b) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4b)
new.d <- apply_labels(new.d, d4b = "until job is done")
temp.d <- data.frame (new.d, d4b)
result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
| Strongly_Agree |
3727 |
63.8 |
65.0 |
63.8 |
65.0 |
| Somewhat_Agree |
1772 |
30.3 |
30.9 |
94.2 |
96.0 |
| Somewhat_Disagree |
189 |
3.2 |
3.3 |
97.4 |
99.2 |
| Strongly_Disagree |
43 |
0.7 |
0.8 |
98.2 |
100.0 |
| NA |
108 |
1.8 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# c. You like doing things that other people thought could not be done.
d4c <- as.factor(d[,"d4c"])
# Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
levels(d4c) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4c)
new.d <- apply_labels(new.d, d4c = "until job is done")
temp.d <- data.frame (new.d, d4c)
result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
| Strongly_Agree |
2751 |
47.1 |
48.3 |
47.1 |
48.3 |
| Somewhat_Agree |
2273 |
38.9 |
39.9 |
86.0 |
88.2 |
| Somewhat_Disagree |
560 |
9.6 |
9.8 |
95.6 |
98.0 |
| Strongly_Disagree |
114 |
2.0 |
2.0 |
97.6 |
100.0 |
| NA |
141 |
2.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# d. When things don’t go the way you want them to, that just makes you work even harder.
d4d <- as.factor(d[,"d4d"])
# Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
levels(d4d) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4d)
new.d <- apply_labels(new.d, d4d = "until job is done")
temp.d <- data.frame (new.d, d4d)
result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
| Strongly_Agree |
2755 |
47.2 |
48.2 |
47.2 |
48.2 |
| Somewhat_Agree |
2404 |
41.2 |
42.1 |
88.4 |
90.3 |
| Somewhat_Disagree |
468 |
8.0 |
8.2 |
96.4 |
98.5 |
| Strongly_Disagree |
84 |
1.4 |
1.5 |
97.8 |
100.0 |
| NA |
128 |
2.2 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
d4e <- as.factor(d[,"d4e"])
# Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
levels(d4e) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4e)
new.d <- apply_labels(new.d, d4e = "do it yourself")
temp.d <- data.frame (new.d, d4e)
result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
| Strongly_Agree |
2445 |
41.9 |
42.7 |
41.9 |
42.7 |
| Somewhat_Agree |
2281 |
39.1 |
39.8 |
80.9 |
82.5 |
| Somewhat_Disagree |
824 |
14.1 |
14.4 |
95.1 |
96.9 |
| Strongly_Disagree |
176 |
3.0 |
3.1 |
98.1 |
100.0 |
| NA |
113 |
1.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
d4f <- as.factor(d[,"d4f"])
# Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
levels(d4f) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4f)
new.d <- apply_labels(new.d, d4f = "not easy but get it done")
temp.d <- data.frame (new.d, d4f)
result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
| Strongly_Agree |
3720 |
63.7 |
64.9 |
63.7 |
64.9 |
| Somewhat_Agree |
1852 |
31.7 |
32.3 |
95.4 |
97.3 |
| Somewhat_Disagree |
116 |
2.0 |
2.0 |
97.4 |
99.3 |
| Strongly_Disagree |
41 |
0.7 |
0.7 |
98.1 |
100.0 |
| NA |
110 |
1.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# g. Very seldom have you been disappointed by the results of your hard work.
d4g <- as.factor(d[,"d4g"])
# Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
levels(d4g) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4g)
new.d <- apply_labels(new.d, d4g = "seldom disappointed")
temp.d <- data.frame (new.d, d4g)
result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
| Strongly_Agree |
2093 |
35.8 |
36.7 |
35.8 |
36.7 |
| Somewhat_Agree |
2633 |
45.1 |
46.2 |
80.9 |
82.9 |
| Somewhat_Disagree |
742 |
12.7 |
13.0 |
93.6 |
95.9 |
| Strongly_Disagree |
233 |
4.0 |
4.1 |
97.6 |
100.0 |
| NA |
138 |
2.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
d4h <- as.factor(d[,"d4h"])
# Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
levels(d4h) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4h)
new.d <- apply_labels(new.d, d4h = "stand up for believes")
temp.d <- data.frame (new.d, d4h)
result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
| Strongly_Agree |
3538 |
60.6 |
61.9 |
60.6 |
61.9 |
| Somewhat_Agree |
1874 |
32.1 |
32.8 |
92.7 |
94.6 |
| Somewhat_Disagree |
257 |
4.4 |
4.5 |
97.1 |
99.1 |
| Strongly_Disagree |
49 |
0.8 |
0.9 |
97.9 |
100.0 |
| NA |
121 |
2.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
# i. In the past, even when things got really tough, you never lost sight of your goals.
d4i <- as.factor(d[,"d4i"])
# Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
levels(d4i) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4i)
new.d <- apply_labels(new.d, d4i = "tough but never lost")
temp.d <- data.frame (new.d, d4i)
result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
| Strongly_Agree |
3279 |
56.2 |
57.3 |
56.2 |
57.3 |
| Somewhat_Agree |
2039 |
34.9 |
35.7 |
91.1 |
93.0 |
| Somewhat_Disagree |
343 |
5.9 |
6.0 |
97.0 |
99.0 |
| Strongly_Disagree |
58 |
1.0 |
1.0 |
97.9 |
100.0 |
| NA |
120 |
2.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
d4j <- as.factor(d[,"d4j"])
# Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
levels(d4j) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4j)
new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
temp.d <- data.frame (new.d, d4j)
result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
| Strongly_Agree |
1987 |
34.0 |
34.7 |
34.0 |
34.7 |
| Somewhat_Agree |
2314 |
39.6 |
40.5 |
73.7 |
75.2 |
| Somewhat_Disagree |
1180 |
20.2 |
20.6 |
93.9 |
95.8 |
| Strongly_Disagree |
238 |
4.1 |
4.2 |
97.9 |
100.0 |
| NA |
120 |
2.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
#k. You don’t let your personal feelings get in the way of doing a job.
d4k <- as.factor(d[,"d4k"])
# Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
levels(d4k) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4k)
new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
temp.d <- data.frame (new.d, d4k)
result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
| Strongly_Agree |
3015 |
51.6 |
52.7 |
51.6 |
52.7 |
| Somewhat_Agree |
2173 |
37.2 |
38.0 |
88.9 |
90.7 |
| Somewhat_Disagree |
429 |
7.3 |
7.5 |
96.2 |
98.1 |
| Strongly_Disagree |
106 |
1.8 |
1.9 |
98.0 |
100.0 |
| NA |
116 |
2.0 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
#l. Hard work has really helped you to get ahead in life.
d4l <- as.factor(d[,"d4l"])
# Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
levels(d4l) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4l)
new.d <- apply_labels(new.d, d4l = "hard work helps")
temp.d <- data.frame (new.d, d4l)
result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
| Strongly_Agree |
3903 |
66.8 |
68.1 |
66.8 |
68.1 |
| Somewhat_Agree |
1466 |
25.1 |
25.6 |
92.0 |
93.7 |
| Somewhat_Disagree |
278 |
4.8 |
4.9 |
96.7 |
98.6 |
| Strongly_Disagree |
83 |
1.4 |
1.4 |
98.1 |
100.0 |
| NA |
109 |
1.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
D5: Childhood
- D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
- Did you live with anyone who was depressed, mentally ill, or suicidal?
- Did you live with anyone who was a problem drinker or alcoholic?
- Did you live with anyone who used illegal street drugs or who abused prescription medications?
- Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
- Were your parents separated or divorced?
- How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
- How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
- How often did a parent or adult in your home ever swear at you, insult you, or put you down?
- How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
- How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
- How often did anyone at least 5 years older than you or an adult, force you to have sex?
- 1=No
- 2=Yes
- 3=Parents not married
- 88=Don’t know/not sure
- 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
d5a <- as.factor(d[,"d5a"])
# Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
levels(d5a) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5a)
new.d <- apply_labels(new.d, d5a = "live with depressed")
temp.d <- data.frame (new.d, d5a)
result<-questionr::freq(temp.d$d5a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
| No |
4739 |
81.2 |
82.5 |
| Yes |
530 |
9.1 |
9.2 |
| Dont_know_not_sure |
423 |
7.2 |
7.4 |
| Prefer_not_to_answer |
51 |
0.9 |
0.9 |
| NA |
96 |
1.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# b. Did you live with anyone who was a problem drinker or alcoholic?
d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
levels(d5b) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5b)
new.d <- apply_labels(new.d, d5b = "live with alcoholic")
temp.d <- data.frame (new.d, d5b)
result<-questionr::freq(temp.d$d5b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
| No |
3942 |
67.5 |
68.6 |
| Yes |
1484 |
25.4 |
25.8 |
| Dont_know_not_sure |
236 |
4.0 |
4.1 |
| Prefer_not_to_answer |
84 |
1.4 |
1.5 |
| NA |
93 |
1.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
levels(d5c) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5c)
new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
temp.d <- data.frame (new.d, d5c)
result<-questionr::freq(temp.d$d5c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
| No |
4863 |
83.3 |
84.8 |
| Yes |
566 |
9.7 |
9.9 |
| Dont_know_not_sure |
249 |
4.3 |
4.3 |
| Prefer_not_to_answer |
60 |
1.0 |
1.0 |
| NA |
101 |
1.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
levels(d5d) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5d)
new.d <- apply_labels(new.d, d5d = "live with people in a prison")
temp.d <- data.frame (new.d, d5d)
result<-questionr::freq(temp.d$d5d,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
| No |
4986 |
85.4 |
86.9 |
| Yes |
614 |
10.5 |
10.7 |
| Dont_know_not_sure |
81 |
1.4 |
1.4 |
| Prefer_not_to_answer |
55 |
0.9 |
1.0 |
| NA |
103 |
1.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# e. Were your parents separated or divorced?
d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
levels(d5e) <- list(No="1",
Yes="2",
Not_married="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5e)
new.d <- apply_labels(new.d, d5e = "parents divorced")
temp.d <- data.frame (new.d, d5e)
result<-questionr::freq(temp.d$d5e,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
| No |
3442 |
58.9 |
60.2 |
| Yes |
1639 |
28.1 |
28.6 |
| Not_married |
468 |
8.0 |
8.2 |
| Dont_know_not_sure |
68 |
1.2 |
1.2 |
| Prefer_not_to_answer |
104 |
1.8 |
1.8 |
| NA |
118 |
2.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
levels(d5f) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5f)
new.d <- apply_labels(new.d, d5f = "violence to each other")
temp.d <- data.frame (new.d, d5f)
result<-questionr::freq(temp.d$d5f,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
| Never |
3569 |
61.1 |
62.7 |
| Once |
375 |
6.4 |
6.6 |
| More_than_once |
905 |
15.5 |
15.9 |
| Dont_know_not_sure |
614 |
10.5 |
10.8 |
| Prefer_not_to_answer |
232 |
4.0 |
4.1 |
| NA |
144 |
2.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
levels(d5g) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5g)
new.d <- apply_labels(new.d, d5g = "violence to you")
temp.d <- data.frame (new.d, d5g)
result<-questionr::freq(temp.d$d5g,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
| Never |
4227 |
72.4 |
74.1 |
| Once |
221 |
3.8 |
3.9 |
| More_than_once |
868 |
14.9 |
15.2 |
| Dont_know_not_sure |
208 |
3.6 |
3.6 |
| Prefer_not_to_answer |
179 |
3.1 |
3.1 |
| NA |
136 |
2.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
levels(d5h) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5h)
new.d <- apply_labels(new.d, d5h = "swear insult")
temp.d <- data.frame (new.d, d5h)
result<-questionr::freq(temp.d$d5h,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
| Never |
3485 |
59.7 |
61.0 |
| Once |
263 |
4.5 |
4.6 |
| More_than_once |
1395 |
23.9 |
24.4 |
| Dont_know_not_sure |
398 |
6.8 |
7.0 |
| Prefer_not_to_answer |
168 |
2.9 |
2.9 |
| NA |
130 |
2.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
d5i <- as.factor(d[,"d5i"])
# Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
levels(d5i) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5i)
new.d <- apply_labels(new.d, d5i = "touch you sexually")
temp.d <- data.frame (new.d, d5i)
result<-questionr::freq(temp.d$d5i,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
| Never |
5134 |
87.9 |
89.6 |
| Once |
248 |
4.2 |
4.3 |
| More_than_once |
197 |
3.4 |
3.4 |
| Dont_know_not_sure |
70 |
1.2 |
1.2 |
| Prefer_not_to_answer |
78 |
1.3 |
1.4 |
| NA |
112 |
1.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
d5j <- as.factor(d[,"d5j"])
# Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
levels(d5j) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5j)
new.d <- apply_labels(new.d, d5j = "touch them sexually")
temp.d <- data.frame (new.d, d5j)
result<-questionr::freq(temp.d$d5j,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
| Never |
5214 |
89.3 |
91.0 |
| Once |
217 |
3.7 |
3.8 |
| More_than_once |
167 |
2.9 |
2.9 |
| Dont_know_not_sure |
57 |
1.0 |
1.0 |
| Prefer_not_to_answer |
75 |
1.3 |
1.3 |
| NA |
109 |
1.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
d5k <- as.factor(d[,"d5k"])
# Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
levels(d5k) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5k)
new.d <- apply_labels(new.d, d5k = "forced to have sex")
temp.d <- data.frame (new.d, d5k)
result<-questionr::freq(temp.d$d5k,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
| Never |
5412 |
92.7 |
94.4 |
| Once |
100 |
1.7 |
1.7 |
| More_than_once |
106 |
1.8 |
1.8 |
| Dont_know_not_sure |
43 |
0.7 |
0.8 |
| Prefer_not_to_answer |
70 |
1.2 |
1.2 |
| NA |
108 |
1.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E1: First indications
- E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
- E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
- E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
- E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
- E1_4: 1=I had bone pain that I went to see my doctor about
- E1_5: 1=I was fearful I had cancer
- E1_6: 1=Other
# 1
e1_1 <- as.factor(d[,"e1_1"])
levels(e1_1) <- list(High_PSA_test="1")
new.d <- data.frame(new.d, e1_1)
new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
temp.d <- data.frame (new.d, e1_1)
result<-questionr::freq(temp.d$e1_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
| High_PSA_test |
4540 |
77.8 |
100 |
| NA |
1299 |
22.2 |
NA |
| Total |
5839 |
100.0 |
100 |
#2
e1_2 <- as.factor(d[,"e1_2"])
levels(e1_2) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_2)
new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
temp.d <- data.frame (new.d, e1_2)
result<-questionr::freq(temp.d$e1_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
| Digital_rectal_exam |
1584 |
27.1 |
100 |
| NA |
4255 |
72.9 |
NA |
| Total |
5839 |
100.0 |
100 |
#3
e1_3 <- as.factor(d[,"e1_3"])
e1_3[which(e1_3=="*")]<-"NA"
levels(e1_3) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_3)
new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
temp.d <- data.frame (new.d, e1_3)
result<-questionr::freq(temp.d$e1_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
| Digital_rectal_exam |
1054 |
18.1 |
100 |
| NA |
4785 |
81.9 |
NA |
| Total |
5839 |
100.0 |
100 |
#4
e1_4 <- as.factor(d[,"e1_4"])
e1_4[which(e1_4=="*")]<-"NA"
levels(e1_4) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_4)
new.d <- apply_labels(new.d, e1_4 = "bone pain")
temp.d <- data.frame (new.d, e1_4)
result<-questionr::freq(temp.d$e1_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
| Digital_rectal_exam |
90 |
1.5 |
100 |
| NA |
5749 |
98.5 |
NA |
| Total |
5839 |
100.0 |
100 |
#5
e1_5 <- as.factor(d[,"e1_5"])
e1_5[which(e1_5=="*")]<-"NA"
levels(e1_5) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_5)
new.d <- apply_labels(new.d, e1_5 = "fearful")
temp.d <- data.frame (new.d, e1_5)
result<-questionr::freq(temp.d$e1_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
| Digital_rectal_exam |
233 |
4 |
100 |
| NA |
5606 |
96 |
NA |
| Total |
5839 |
100 |
100 |
E1 Other: First indications
e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"
new.d <- data.frame(new.d, e1other)
new.d <- apply_labels(new.d, e1other = "e1other")
temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "E1 Other")
E1 Other
| 2 cousins died with PC before my increasing PSA test |
1 |
0.0 |
0.2 |
| 3 maternal uncles have prostate cancer |
1 |
0.0 |
0.2 |
| 51 or 52 |
1 |
0.0 |
0.2 |
| A bladder infection |
1 |
0.0 |
0.2 |
| A friend knew of my pain and suggested I attent Prostate Survisors group |
1 |
0.0 |
0.2 |
| A low blood count |
1 |
0.0 |
0.2 |
| A sopt was observed on my Lung after a CT Scand that turned out to be Metastatic. I was diagnosed after receiving a Postate Biopsy. |
1 |
0.0 |
0.2 |
| Accident. Took father-in-law to doctor. Asked me if he examine me. I said yes. That’s it. |
1 |
0.0 |
0.2 |
| Acupuncturist referred me to my doctor. |
1 |
0.0 |
0.2 |
| After bladder infection |
1 |
0.0 |
0.2 |
| After having many high PSA blood tests I got a positive biopsy test. |
1 |
0.0 |
0.2 |
| After I had been hurt moving a heavy object is when I found out I had prostate cancer. |
1 |
0.0 |
0.2 |
| After my oldest brother died of cancer, my doctor suggested a biopsy |
1 |
0.0 |
0.2 |
| Age 50 I took a blood test |
1 |
0.0 |
0.2 |
| age and race |
1 |
0.0 |
0.2 |
| Agent Orange Vietnam 1972 |
1 |
0.0 |
0.2 |
| Always weak. |
1 |
0.0 |
0.2 |
| An older brother. Tells me what was the symptoms of prostate cancer and made an appointment to see a doctor. |
1 |
0.0 |
0.2 |
| And was feeling pain below my belly and frequent urination |
1 |
0.0 |
0.2 |
| annual exam |
1 |
0.0 |
0.2 |
| Annual Examination |
1 |
0.0 |
0.2 |
| Annual physical |
3 |
0.1 |
0.7 |
| As a result of a physical exam |
1 |
0.0 |
0.2 |
| As a Vietnam Veteran, I was assigned to an agent orange area. |
1 |
0.0 |
0.2 |
| Asked for check-up. |
1 |
0.0 |
0.2 |
| At a routine check-up. |
1 |
0.0 |
0.2 |
| At my age, was advised to have exam. |
1 |
0.0 |
0.2 |
| Back pain, getting married blood test. |
1 |
0.0 |
0.2 |
| Back pain/stomach pain |
1 |
0.0 |
0.2 |
| Back problems. |
1 |
0.0 |
0.2 |
| Be check |
1 |
0.0 |
0.2 |
| Bence Jones protein |
1 |
0.0 |
0.2 |
| biopsy |
1 |
0.0 |
0.2 |
| Biopsy |
1 |
0.0 |
0.2 |
| Biopsy, MRI |
1 |
0.0 |
0.2 |
| Bladder condition. |
1 |
0.0 |
0.2 |
| Bladder Neck Surgery, Prostate Resection |
1 |
0.0 |
0.2 |
| Bled once during |
1 |
0.0 |
0.2 |
| Bleeding from rectum/blood in urine |
1 |
0.0 |
0.2 |
| Bleeding in urine after sex. |
1 |
0.0 |
0.2 |
| Blood drive |
1 |
0.0 |
0.2 |
| Blood in my ejaculation/sperm |
1 |
0.0 |
0.2 |
| Blood in my urine |
1 |
0.0 |
0.2 |
| Blood in my urine January 2015. |
1 |
0.0 |
0.2 |
| Blood in semen |
1 |
0.0 |
0.2 |
| Blood in semen fluid |
1 |
0.0 |
0.2 |
| Blood in semen. |
1 |
0.0 |
0.2 |
| Blood in the toilet after eliminating |
1 |
0.0 |
0.2 |
| Blood in urin |
1 |
0.0 |
0.2 |
| Blood in urine |
8 |
0.1 |
1.9 |
| Blood in urine (from light pink to straight blood). |
1 |
0.0 |
0.2 |
| Blood in urine. |
2 |
0.0 |
0.5 |
| Blood test |
1 |
0.0 |
0.2 |
| Blood test positive for cancer |
1 |
0.0 |
0.2 |
| Blood test. |
2 |
0.0 |
0.5 |
| Blood work colonoscopy. |
1 |
0.0 |
0.2 |
| bloody semen |
1 |
0.0 |
0.2 |
| bloody urine |
1 |
0.0 |
0.2 |
| Body scan MRI |
1 |
0.0 |
0.2 |
| Both brothers had been recently diagnosed |
1 |
0.0 |
0.2 |
| Brother diagnosed |
1 |
0.0 |
0.2 |
| Brother just diagnosed |
1 |
0.0 |
0.2 |
| Brother previously diagnosed |
1 |
0.0 |
0.2 |
| Burning sensation |
1 |
0.0 |
0.2 |
| Burning sensation in prostate area |
1 |
0.0 |
0.2 |
| Can’t remember. I had something done at the doctor’s office, I think. |
1 |
0.0 |
0.2 |
| Charles Briley, Sr. (Father) died stage 4 prostate cancer |
1 |
0.0 |
0.2 |
| Check up |
1 |
0.0 |
0.2 |
| Check up from new doctor. |
1 |
0.0 |
0.2 |
| Colon. |
1 |
0.0 |
0.2 |
| Complications from groin hernia surgery. |
1 |
0.0 |
0.2 |
| Could not hold urine |
1 |
0.0 |
0.2 |
| Couldn’t finish urinating |
1 |
0.0 |
0.2 |
| Couldn’t have sex |
1 |
0.0 |
0.2 |
| Cut in —- during surgery/cancer form infection |
1 |
0.0 |
0.2 |
| Did not have a clue. |
1 |
0.0 |
0.2 |
| Didn’t know I had it |
1 |
0.0 |
0.2 |
| Didn’t know! It was by mistake they found it. |
1 |
0.0 |
0.2 |
| Difficulty urinating |
1 |
0.0 |
0.2 |
| Digital exam showed no abnormality in size of prostate |
1 |
0.0 |
0.2 |
| Discomfort in the area |
1 |
0.0 |
0.2 |
| Doctor did PSA at exam |
1 |
0.0 |
0.2 |
| Doctor suggested evaluation because of my age |
1 |
0.0 |
0.2 |
| Doctor tested me and did not find anything, then tested me again PSA was high. |
1 |
0.0 |
0.2 |
| Doctor told me |
1 |
0.0 |
0.2 |
| Doctor, unusual count, not sure, ten years later found out. |
1 |
0.0 |
0.2 |
| Doctors check up |
1 |
0.0 |
0.2 |
| Doctors exam when I coughed |
1 |
0.0 |
0.2 |
| Don’t know |
1 |
0.0 |
0.2 |
| Don’t know. |
1 |
0.0 |
0.2 |
| Don’t know’ |
1 |
0.0 |
0.2 |
| Don’t remember |
1 |
0.0 |
0.2 |
| Dr. asked about family history of prostate cancer |
1 |
0.0 |
0.2 |
| During annual routine check. |
1 |
0.0 |
0.2 |
| ED |
1 |
0.0 |
0.2 |
| elevated PSA on life insurance test |
1 |
0.0 |
0.2 |
| Enlarge prostate |
2 |
0.0 |
0.5 |
| Enlarged prostate |
3 |
0.1 |
0.7 |
| Enlarged Prostate caused frequent trips to bathroom durning the night |
1 |
0.0 |
0.2 |
| Enlarged prostate surgery 1991 |
1 |
0.0 |
0.2 |
| Enlarged prostrate. |
1 |
0.0 |
0.2 |
| ER dysfunction |
1 |
0.0 |
0.2 |
| Erection did not stay up to long |
1 |
0.0 |
0.2 |
| Examination. |
1 |
0.0 |
0.2 |
| Exercising walking leg start hurting. |
1 |
0.0 |
0.2 |
| Exposed to agent orange Vietnam June 1970 to May 1971 |
1 |
0.0 |
0.2 |
| Extreme dizzy spell lead to doctor visit/diagnosis |
1 |
0.0 |
0.2 |
| Eye exam started the process of going to a doctor for 1st time in 25 years |
1 |
0.0 |
0.2 |
| Eye pain. Doctor told me to go to my GP (general practitioner). GP said/tested for prostate. |
1 |
0.0 |
0.2 |
| Family history |
5 |
0.1 |
1.2 |
| Family History |
1 |
0.0 |
0.2 |
| Family history. |
1 |
0.0 |
0.2 |
| Father had it. |
1 |
0.0 |
0.2 |
| Father had prostate cancer and died. |
1 |
0.0 |
0.2 |
| Fatigue |
1 |
0.0 |
0.2 |
| feeling having to urinate but wouldn’t actually go |
1 |
0.0 |
0.2 |
| Found during my yearly check up |
1 |
0.0 |
0.2 |
| Found during urinary problem, frequent urination surgery on prostate —- |
1 |
0.0 |
0.2 |
| Found out through kidney stone treatment |
1 |
0.0 |
0.2 |
| Frequent time urination |
1 |
0.0 |
0.2 |
| Frequent urination |
4 |
0.1 |
1.0 |
| Frequent urination at night during sleeping hours. |
1 |
0.0 |
0.2 |
| Frequent urination; no real symptoms. |
1 |
0.0 |
0.2 |
| Frequent urination. |
2 |
0.0 |
0.5 |
| Frequent urination. Told Dr. during regular physical. He prescribed medication (tablet) for it. At next physical asid urinating more often & asked to see Urologist. He agreed. Urologist did biopsy. |
1 |
0.0 |
0.2 |
| From a regular blood test. |
1 |
0.0 |
0.2 |
| General check up Dr. noticed raised abdomen |
1 |
0.0 |
0.2 |
| Get sick. |
1 |
0.0 |
0.2 |
| Got health insurance |
1 |
0.0 |
0.2 |
| Got very sick |
1 |
0.0 |
0.2 |
| Groin Pain |
1 |
0.0 |
0.2 |
| groin pain and discomfort |
1 |
0.0 |
0.2 |
| Gut feeling |
1 |
0.0 |
0.2 |
| Had a biopsy that came out positive. |
1 |
0.0 |
0.2 |
| Had a check up with doctor when I found out. |
1 |
0.0 |
0.2 |
| Had a slight fever and A UTI |
1 |
0.0 |
0.2 |
| Had an X-Ray done |
1 |
0.0 |
0.2 |
| Had bladder stones and an enlarged prostate. Note: Doctors at LSU Health Shreveport said it was the largest prostate they had ever seen before when I had it removed for prostate cancer in 2015. |
1 |
0.0 |
0.2 |
| Had blood in my urine. |
1 |
0.0 |
0.2 |
| Had blood in bowel went to see doctor |
1 |
0.0 |
0.2 |
| had blood test and PSA test was high |
1 |
0.0 |
0.2 |
| had cancer in kidney and further examinations indicated prostate cancer |
1 |
0.0 |
0.2 |
| Had fluctuating PSA |
1 |
0.0 |
0.2 |
| Had gotten very ill. |
1 |
0.0 |
0.2 |
| Had heat stroke went hospital and was sent to see my doctor. |
1 |
0.0 |
0.2 |
| had insanely intense and debilitating lower back pain…couldn’t walk or even move at times. Covered a span of 3 days before wife insisted I see a doctor. PSA test was taken at that time. |
1 |
0.0 |
0.2 |
| Had no idea |
1 |
0.0 |
0.2 |
| Had serious problems urinating. |
1 |
0.0 |
0.2 |
| Had swelling in leg, blood clot. |
1 |
0.0 |
0.2 |
| Had to make several trips to the restroom. |
1 |
0.0 |
0.2 |
| Had to urinate often |
1 |
0.0 |
0.2 |
| Had trauma-stomach surgery, blood work Oct. 2014 |
1 |
0.0 |
0.2 |
| He did blood test to find out. |
1 |
0.0 |
0.2 |
| Heavy pain in my right kidney |
1 |
0.0 |
0.2 |
| Hereditary |
1 |
0.0 |
0.2 |
| high protein |
1 |
0.0 |
0.2 |
| High protein level in urine. |
1 |
0.0 |
0.2 |
| History of my father, made me self conscious at early age! |
1 |
0.0 |
0.2 |
| Hurt to urinate |
1 |
0.0 |
0.2 |
| I also had an enlarged prostate. |
1 |
0.0 |
0.2 |
| I also had rectal exam |
1 |
0.0 |
0.2 |
| I am not sure |
1 |
0.0 |
0.2 |
| I applied for ins. they did a PSA test, not my Dr. |
1 |
0.0 |
0.2 |
| I did a physical the first since I came to America in 2015 and my primary physician said my PSA needed further investigation. |
1 |
0.0 |
0.2 |
| I did not know. |
1 |
0.0 |
0.2 |
| I did test every 6 months missed 1 yr 6 months (my wife had cancer). |
1 |
0.0 |
0.2 |
| I felt something that felt like someone was moving their fingers across my abdomen as if they were trying to tickle me |
1 |
0.0 |
0.2 |
| I found blood in my urine. |
1 |
0.0 |
0.2 |
| I had a prostate infection 6 months prior to my diagnosis |
1 |
0.0 |
0.2 |
| I had a tingling feeling in my scrotum. |
1 |
0.0 |
0.2 |
| I had an enlarged prostate (urinating a lot), so from there it was diagnosed. |
1 |
0.0 |
0.2 |
| i had blood in my ejaculate |
1 |
0.0 |
0.2 |
| I had blood in my semen. |
1 |
0.0 |
0.2 |
| I had blood in my urine. |
1 |
0.0 |
0.2 |
| I had enlarged prostate during one of my routine check ups, my doctor found out that my PSA was high. |
1 |
0.0 |
0.2 |
| I had know clue. |
1 |
0.0 |
0.2 |
| I had lab work done my PSA was high |
1 |
0.0 |
0.2 |
| I had no clue |
1 |
0.0 |
0.2 |
| I had no indication. |
1 |
0.0 |
0.2 |
| I had no indications that I had prostate cancer. |
1 |
0.0 |
0.2 |
| I had no prior indications. I went to a health fair a couple of months after a routine physical and the #s were different, so after comparing the #s I was sent to a specialist. |
1 |
0.0 |
0.2 |
| I had no symptoms |
1 |
0.0 |
0.2 |
| I had no symptoms at all |
1 |
0.0 |
0.2 |
| I had no symptoms had a rectal exam and doctor felt an abnormality he suggested a biopsy |
1 |
0.0 |
0.2 |
| I had problems passing my urine. |
1 |
0.0 |
0.2 |
| I had prostrte surgery |
1 |
0.0 |
0.2 |
| I had to urinate every 45 minutes during an outing with a friend. |
1 |
0.0 |
0.2 |
| I had urinary |
1 |
0.0 |
0.2 |
| I just ask my doctor when do I see her. |
1 |
0.0 |
0.2 |
| I just thought I had bladder problems no cancer |
1 |
0.0 |
0.2 |
| I kept having to pee all the time. |
1 |
0.0 |
0.2 |
| I need to be tested. I knew that at age 35 and up. |
1 |
0.0 |
0.2 |
| I never had a PSA test done so she requested one. |
1 |
0.0 |
0.2 |
| I previously applied for insurance policy |
1 |
0.0 |
0.2 |
| I started having erectile disfunction |
1 |
0.0 |
0.2 |
| I started having urination issues, very frequent and hard to pass. |
1 |
0.0 |
0.2 |
| I told my primary doctor of my family history of Cancer |
1 |
0.0 |
0.2 |
| I took a blood test for a life inurance plicy and my PSA came back high. |
1 |
0.0 |
0.2 |
| I urinate a lot |
1 |
0.0 |
0.2 |
| I wanted a PSA test (me) |
1 |
0.0 |
0.2 |
| I wanted to buy life insurance the doctor tested me. |
1 |
0.0 |
0.2 |
| i was week and just not feeling myself. |
1 |
0.0 |
0.2 |
| I was admitted to the hospital because I was very sick that’s when they found out that I have cancer. |
1 |
0.0 |
0.2 |
| I was experiencing leakage of urine |
1 |
0.0 |
0.2 |
| I was having trouble urinating |
1 |
0.0 |
0.2 |
| I was in so much pain |
1 |
0.0 |
0.2 |
| i was speaking with a co-worker about his cancer experience |
1 |
0.0 |
0.2 |
| I was told to get checked once a year when I got 45 years old |
1 |
0.0 |
0.2 |
| I went for my exam. |
1 |
0.0 |
0.2 |
| Im good now |
1 |
0.0 |
0.2 |
| in the family of the men |
1 |
0.0 |
0.2 |
| Increased blood pressure during sex |
1 |
0.0 |
0.2 |
| Increased intensity lower back pain that I went to my doctor about |
1 |
0.0 |
0.2 |
| increased urinary frequency |
1 |
0.0 |
0.2 |
| Insurance questioned psa test results |
1 |
0.0 |
0.2 |
| Kept having to go to the bathroom |
1 |
0.0 |
0.2 |
| Kidney program tested my blood |
1 |
0.0 |
0.2 |
| kidney stone |
1 |
0.0 |
0.2 |
| Knew I felt different, but found out through my doctor. |
1 |
0.0 |
0.2 |
| Knowledge of family history and risk |
1 |
0.0 |
0.2 |
| Large prostate, frequent urination |
1 |
0.0 |
0.2 |
| Leg pain |
1 |
0.0 |
0.2 |
| Life ins. blood test |
1 |
0.0 |
0.2 |
| Loss of energy weight gain |
1 |
0.0 |
0.2 |
| Loss of weight |
2 |
0.0 |
0.5 |
| Lost 50 pounds fast |
1 |
0.0 |
0.2 |
| Lost a lot of weight |
1 |
0.0 |
0.2 |
| Low energy levels at times. |
1 |
0.0 |
0.2 |
| Low flow could not go to the bathroom |
1 |
0.0 |
0.2 |
| Low level fever fatigue. |
1 |
0.0 |
0.2 |
| Low testosterone levels |
1 |
0.0 |
0.2 |
| Lower back pain |
1 |
0.0 |
0.2 |
| Lumps found in colonoscopy |
1 |
0.0 |
0.2 |
| medical exam |
1 |
0.0 |
0.2 |
| MRI Test |
1 |
0.0 |
0.2 |
| MRI. |
1 |
0.0 |
0.2 |
| My 2 brothers, my grandfather and his brother had prostate cancer |
1 |
0.0 |
0.2 |
| My A1C was high and they wanted to test me |
1 |
0.0 |
0.2 |
| My blood PSA was high |
1 |
0.0 |
0.2 |
| My body was reacting abnormally for a period of time. |
1 |
0.0 |
0.2 |
| My brother developed prostate cancer/high PSA test |
1 |
0.0 |
0.2 |
| My brother got prostate cancer |
1 |
0.0 |
0.2 |
| my brother had prostate cancer |
1 |
0.0 |
0.2 |
| My brother had prostate cancer |
1 |
0.0 |
0.2 |
| My brother was diagnosed, so I got tested. I also had an enlarged prostate for years |
1 |
0.0 |
0.2 |
| My brothers had it. |
1 |
0.0 |
0.2 |
| My Doc. sent me for an exam because of my age. |
1 |
0.0 |
0.2 |
| My doctor did an exam and determined my prostate was enlarged. |
1 |
0.0 |
0.2 |
| My doctor gave me a test and seen the signs of it, Random check up. |
1 |
0.0 |
0.2 |
| My doctor was concerned with my enlarged prostate |
1 |
0.0 |
0.2 |
| My dog Jackson kept poking my groin with his nose there stare at me. |
1 |
0.0 |
0.2 |
| My dog sniffed and warned me |
1 |
0.0 |
0.2 |
| My family died of cancer. |
1 |
0.0 |
0.2 |
| My father and grandfather died from cancer. |
1 |
0.0 |
0.2 |
| My father died of prostate cancer. |
1 |
0.0 |
0.2 |
| My father had it, so I was tested. |
1 |
0.0 |
0.2 |
| My father told me to get checked because I wasn’t the age for it and because it runs in the men in his family. |
1 |
0.0 |
0.2 |
| My Father was diagnosed with Prostate Cancer |
1 |
0.0 |
0.2 |
| My father was diagnosed with prostate cancer. |
1 |
0.0 |
0.2 |
| My fathers diagnosis had me on alert |
1 |
0.0 |
0.2 |
| My glands were swollen. |
1 |
0.0 |
0.2 |
| My great uncle had it. I became aware and had check ups. |
1 |
0.0 |
0.2 |
| My life insurance company rejected me for life insurance |
1 |
0.0 |
0.2 |
| My new doctor asked if I ever seen a Urologist, I said no and he said I should. My PSA was 7. Had never had one. |
1 |
0.0 |
0.2 |
| my older brother was diagnosed and suggested I do the same. |
1 |
0.0 |
0.2 |
| My PCP found blood in my urine test. |
1 |
0.0 |
0.2 |
| My primary care physician referred me routinely. |
1 |
0.0 |
0.2 |
| my primary Dr suggested I see a Urologist |
1 |
0.0 |
0.2 |
| My PSA numbers |
1 |
0.0 |
0.2 |
| My PSA was being checked on my regular doctor visits. |
1 |
0.0 |
0.2 |
| my psa was between 8-10 |
1 |
0.0 |
0.2 |
| My PSA was high. |
1 |
0.0 |
0.2 |
| My right testicle was swollen |
1 |
0.0 |
0.2 |
| My semen ejaculation decreased to almost none. |
1 |
0.0 |
0.2 |
| My sex life is over, no more sex. |
1 |
0.0 |
0.2 |
| My urologist wanted me to take a specific test for prostate cancer other than the PSA test |
1 |
0.0 |
0.2 |
| “NA” |
1 |
0.0 |
0.2 |
| Never |
1 |
0.0 |
0.2 |
| Never been tested in my sixty |
1 |
0.0 |
0.2 |
| New doctor noticed no PSA test with/Testosterone shots |
1 |
0.0 |
0.2 |
| No indication |
1 |
0.0 |
0.2 |
| No indication with regular check ups |
1 |
0.0 |
0.2 |
| No indications, no signs |
1 |
0.0 |
0.2 |
| No sign ever |
1 |
0.0 |
0.2 |
| No symptoms |
1 |
0.0 |
0.2 |
| No. |
2 |
0.0 |
0.5 |
| nodule in prostate |
1 |
0.0 |
0.2 |
| Nodule noticed on prostate during colonoscopy. |
1 |
0.0 |
0.2 |
| Nodules on groin |
1 |
0.0 |
0.2 |
| none |
1 |
0.0 |
0.2 |
| None |
2 |
0.0 |
0.5 |
| None I just had pain |
1 |
0.0 |
0.2 |
| NONE, doctor just had me take the PSA test. |
1 |
0.0 |
0.2 |
| Normal urological blood work showed-PSA |
1 |
0.0 |
0.2 |
| On 2-28-2016 10:15 AM PSA was 6354 (block) |
1 |
0.0 |
0.2 |
| On my double 18 wheeler truck accident, I had multiple internal injuries that is how in one of the test doctor said it looks like you may have —- |
1 |
0.0 |
0.2 |
| Other family members had prostate cancer |
1 |
0.0 |
0.2 |
| Over a time PSA was getting higher. |
1 |
0.0 |
0.2 |
| PA performed the finger test & referred me to an Urology Doctor |
1 |
0.0 |
0.2 |
| Pain in my groin. |
1 |
0.0 |
0.2 |
| Pain in my testicles |
1 |
0.0 |
0.2 |
| Pain in reproductive area |
1 |
0.0 |
0.2 |
| Pain in right leg, fearful did not know why. |
1 |
0.0 |
0.2 |
| Passing blood after sex. |
1 |
0.0 |
0.2 |
| PCA3 test |
1 |
0.0 |
0.2 |
| people’s have told me for my ages also because I was taking zantecs |
1 |
0.0 |
0.2 |
| peripheral vascular problems |
1 |
0.0 |
0.2 |
| PET Scan |
1 |
0.0 |
0.2 |
| Physical |
1 |
0.0 |
0.2 |
| Physical and finger up posterior |
1 |
0.0 |
0.2 |
| physical exam |
1 |
0.0 |
0.2 |
| Physical exam. |
1 |
0.0 |
0.2 |
| Physical Rectal Exam |
1 |
0.0 |
0.2 |
| Physical. |
1 |
0.0 |
0.2 |
| Pre Hernia surgery |
1 |
0.0 |
0.2 |
| Preparing for biopsy surgery and current psa was compared to 4 previous and found to be extremely high. |
1 |
0.0 |
0.2 |
| Primary doctor concern about result of blood work |
1 |
0.0 |
0.2 |
| Problem urinating |
1 |
0.0 |
0.2 |
| Procedure for bladder |
1 |
0.0 |
0.2 |
| prostate cancer were discovered during my urinary tract surgery. A cancer surgeon was called in to remove all microscopically visible cancer cells |
1 |
0.0 |
0.2 |
| Prostate study program at Henry Ford Hospital (1980’s). |
1 |
0.0 |
0.2 |
| Prostate swell |
1 |
0.0 |
0.2 |
| Prostate was large |
1 |
0.0 |
0.2 |
| PSA levels kept rising. |
1 |
0.0 |
0.2 |
| PSA reading was above normal not significantly higher than normal |
1 |
0.0 |
0.2 |
| PSA test |
1 |
0.0 |
0.2 |
| PSA testing and education program |
1 |
0.0 |
0.2 |
| PSA went from 14 to 45. |
1 |
0.0 |
0.2 |
| Rapid weight loss |
1 |
0.0 |
0.2 |
| Ref. by my medical doctor to see urologist. |
1 |
0.0 |
0.2 |
| Regular check up |
2 |
0.0 |
0.5 |
| Regular physical examination |
1 |
0.0 |
0.2 |
| Regular prostate check up felt a lump |
1 |
0.0 |
0.2 |
| Routine check-up. |
1 |
0.0 |
0.2 |
| Routine checkup by my primary care doctor |
1 |
0.0 |
0.2 |
| Routine exam |
2 |
0.0 |
0.5 |
| Routine Exam |
1 |
0.0 |
0.2 |
| Routine rectal exam |
1 |
0.0 |
0.2 |
| Scrotal pain of unknown origin |
1 |
0.0 |
0.2 |
| Semen in urine |
1 |
0.0 |
0.2 |
| Sexual issues |
1 |
0.0 |
0.2 |
| Sharp pain in right lower region. |
1 |
0.0 |
0.2 |
| Sharp pains in the groin area |
1 |
0.0 |
0.2 |
| Slip and fall exam. |
1 |
0.0 |
0.2 |
| Slow stream, but normal PSA |
1 |
0.0 |
0.2 |
| Something was discovered during routine health check up. |
1 |
0.0 |
0.2 |
| Stage 2 radiation it’s negative |
1 |
0.0 |
0.2 |
| Starting having erection problems |
1 |
0.0 |
0.2 |
| Strong family history |
1 |
0.0 |
0.2 |
| Strong Family History, Physician did not listen |
1 |
0.0 |
0.2 |
| Sweating |
1 |
0.0 |
0.2 |
| Swollen and tender to touch |
1 |
0.0 |
0.2 |
| Terrible family history. Mother had nine brothers, seven had prostate cancer. |
1 |
0.0 |
0.2 |
| Testicle pain |
1 |
0.0 |
0.2 |
| testing done |
1 |
0.0 |
0.2 |
| The Dr. just told me at a check up! I always wonder if that was a scam! |
1 |
0.0 |
0.2 |
| The nurse of Cosmos detected an abnormality during her test |
1 |
0.0 |
0.2 |
| There was traces of blood in my urine. |
1 |
0.0 |
0.2 |
| Things went good |
1 |
0.0 |
0.2 |
| Thought it was upset stomach and having pain. |
1 |
0.0 |
0.2 |
| To the DR for stomach pains |
1 |
0.0 |
0.2 |
| Tumor on right scrotum |
1 |
0.0 |
0.2 |
| TURP performed for LUTS. |
1 |
0.0 |
0.2 |
| Ultra sound of prostate |
1 |
0.0 |
0.2 |
| Unknown back pain |
1 |
0.0 |
0.2 |
| Up & down PSA |
1 |
0.0 |
0.2 |
| Urgency to urinate, Frequent urination |
1 |
0.0 |
0.2 |
| Urinary |
1 |
0.0 |
0.2 |
| Urinated frequently |
1 |
0.0 |
0.2 |
| Urinating slow |
1 |
0.0 |
0.2 |
| Urinating too much. |
1 |
0.0 |
0.2 |
| Vietnam was Agent Orange |
1 |
0.0 |
0.2 |
| Visible blood in ejaculate (once). |
1 |
0.0 |
0.2 |
| Waking up thru the night using the bathroom often |
1 |
0.0 |
0.2 |
| Weak urine stream, PSA of 13 on regular medical blood test |
1 |
0.0 |
0.2 |
| Weakness and loss of weight. |
1 |
0.0 |
0.2 |
| Weight loss |
1 |
0.0 |
0.2 |
| Went for normal lab test, blood in urine and came out then. |
1 |
0.0 |
0.2 |
| went for testing because insurance company required it |
1 |
0.0 |
0.2 |
| Went to doctor for check up |
1 |
0.0 |
0.2 |
| When I had my yearly physical |
1 |
0.0 |
0.2 |
| When VA check me |
1 |
0.0 |
0.2 |
| While in USAF, was a DaNang AB Vietnam mid 1965 |
1 |
0.0 |
0.2 |
| With no actual symptoms |
1 |
0.0 |
0.2 |
| Yearly check-up. |
1 |
0.0 |
0.2 |
| NA |
5427 |
92.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E2: Before diagnosis
- E2. Before you were diagnosed with prostate cancer:
- Did you have any previous prostate biopsies that were negative?
- If yes, How many?
- Did you have any previous PSA blood tests that were considered normal?
- If yes, How many?
- 1=1
- 2=2
- 3=3
- 4=4
- 5=5 or more
# 1
e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
levels(e2aa) <- list(Yes="2",
No="1",
Dont_know="88")
e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
new.d <- data.frame(new.d, e2aa)
new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
temp.d <- data.frame (new.d, e2aa)
result<-questionr::freq(temp.d$e2aa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
| Yes |
765 |
13.1 |
13.6 |
| No |
4282 |
73.3 |
76.2 |
| Dont_know |
576 |
9.9 |
10.2 |
| NA |
216 |
3.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#2
e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
levels(e2ab) <- list(One="1",
Two="2",
Three_more="3")
e2ab <- ordered(e2ab, c("One","Two","Three_more"))
new.d <- data.frame(new.d, e2ab)
new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
temp.d <- data.frame (new.d, e2ab)
result<-questionr::freq(temp.d$e2ab,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
| One |
419 |
7.2 |
48.6 |
| Two |
234 |
4.0 |
27.1 |
| Three_more |
209 |
3.6 |
24.2 |
| NA |
4977 |
85.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#3
e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
levels(e2ba) <- list(Yes="2",
No="1",
Dont_know="88")
e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
new.d <- data.frame(new.d, e2ba)
new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
temp.d <- data.frame (new.d, e2ba)
result<-questionr::freq(temp.d$e2ba,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
| Yes |
2589 |
44.3 |
48.2 |
| No |
1423 |
24.4 |
26.5 |
| Dont_know |
1354 |
23.2 |
25.2 |
| NA |
473 |
8.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
#4
e2bb <- as.factor(d[,"e2bb"])
# Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
levels(e2bb) <- list(One="1",
Two="2",
Three="3",
Four="4",
Five_more="5")
e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
new.d <- data.frame(new.d, e2bb)
new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
temp.d <- data.frame (new.d, e2bb)
result<-questionr::freq(temp.d$e2bb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
| One |
264 |
4.5 |
12.0 |
| Two |
417 |
7.1 |
19.0 |
| Threem |
0 |
0.0 |
0.0 |
| Four |
275 |
4.7 |
12.5 |
| Five_more |
1241 |
21.3 |
56.5 |
| NA |
3642 |
62.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E3: Decision about PSA blood test
- E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
- 1=I made the decision alone
- 2=I made the decision together with a family member or friend
- 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
- 4= I made the decision together with my doctor, nurse, or health care provider
- 5=My doctor, nurse, or health care provider made the decision
- 88=I do not know or remember how the decision was made
e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
levels(e3) <- list(Alone="1",
With_family_or_friends="2",
With_family_and_doctor="3",
With_doctor="4",
Doctor_made="5",
Dont_know_or_remember="88")
e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
new.d <- data.frame(new.d, e3)
new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
temp.d <- data.frame (new.d, e3)
result<-questionr::freq(temp.d$e3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "E3")
E3
| Alone |
861 |
14.7 |
15.4 |
| With_family_or_friends |
365 |
6.3 |
6.5 |
| With_family_and_doctor |
791 |
13.5 |
14.1 |
| With_doctor |
1519 |
26.0 |
27.1 |
| Doctor_made |
1737 |
29.7 |
31.0 |
| Dont_know_or_remember |
323 |
5.5 |
5.8 |
| NA |
243 |
4.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E4: Understanding of aggressiveness
- E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
- 1=Low risk of progression
- 2=Intermediate risk of progression
- 3=High risk of progression
- 4=Unknown risk of progression
- 88=Don’t know/Don’t remember
e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
levels(e4) <- list(Low_risk="1",
Intermediate_risk="2",
High_risk="3",
Unknown_risk="4",
Dont_know_or_remember="88")
e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
new.d <- data.frame(new.d, e4)
new.d <- apply_labels(new.d, e4 = "how aggressive")
temp.d <- data.frame (new.d, e4)
result<-questionr::freq(temp.d$e4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e4")
e4
| Low_risk |
2040 |
34.9 |
35.6 |
| Intermediate_risk |
1021 |
17.5 |
17.8 |
| High_risk |
1334 |
22.8 |
23.3 |
| Unknown_risk |
587 |
10.1 |
10.3 |
| Dont_know_or_remember |
743 |
12.7 |
13.0 |
| NA |
114 |
2.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E5: Gleason score
- E5. What was your Gleason score when you were diagnosed with prostate cancer?
- 1=6 or less
- 2=7
- 3=8-10
- 88=Don’t know
e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
levels(e5) <- list(Six_less="1",
Seven="2",
Eight_to_ten="3",
Dont_know="88")
e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
new.d <- data.frame(new.d, e5)
new.d <- apply_labels(new.d, e5 = "Gleason score")
temp.d <- data.frame (new.d, e5)
result<-questionr::freq(temp.d$e5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e5")
e5
| Six_less |
1145 |
19.6 |
20.3 |
| Seven |
1003 |
17.2 |
17.8 |
| Eight_to_ten |
856 |
14.7 |
15.2 |
| Dont_know |
2634 |
45.1 |
46.7 |
| NA |
201 |
3.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E6: Understanding of stage
- E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
- 1=Localized, confined to prostate
- 2=Regional, tumor extended to regions around the prostate
- 3=Distant, tumor extended to bones or other parts of body
- 88=Don’t know about the stage
e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
levels(e6) <- list(Localized="1",
Regional="2",
Distant="3",
Dont_know="88")
e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
new.d <- data.frame(new.d, e6)
new.d <- apply_labels(new.d, e6 = "Stage")
temp.d <- data.frame (new.d, e6)
result<-questionr::freq(temp.d$e6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e6")
e6
| Localized |
4035 |
69.1 |
71.0 |
| Regional |
276 |
4.7 |
4.9 |
| Distant |
114 |
2.0 |
2.0 |
| Dont_know |
1256 |
21.5 |
22.1 |
| NA |
158 |
2.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E7: MRI guided biopsy
- E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
levels(e7) <- list(No="1",
Yes="2",
Dont_know="88")
e7 <- ordered(e7, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e7)
new.d <- apply_labels(new.d, e7 = "Stage")
temp.d <- data.frame (new.d, e7)
result<-questionr::freq(temp.d$e7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e7")
e7
| No |
2202 |
37.7 |
38.9 |
| Yes |
2154 |
36.9 |
38.1 |
| Dont_know |
1304 |
22.3 |
23.0 |
| NA |
179 |
3.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E8: Decision about treatment
- E8. How did you make your treatment decision?
- 1=I made the decision alone
- 2=I made the decision together with a family member or friend
- 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
- 4=I made the decision together with my doctor, nurse, or health care provider
- 5=My doctor , nurse, or health care provider made the decision
- 6=I don’t know or remember how the decision was made
e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
levels(e8) <- list(Alone="1",
With_family_or_friends="2",
With_family_and_doctor="3",
With_doctor="4",
Doctor_made="5",
Dont_know_or_remember="88")
e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
new.d <- data.frame(new.d, e8)
new.d <- apply_labels(new.d, e8 = "treatment decision")
temp.d <- data.frame (new.d, e8)
result<-questionr::freq(temp.d$e8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e8")
e8
| Alone |
796 |
13.6 |
14.4 |
| With_family_or_friends |
834 |
14.3 |
15.1 |
| With_family_and_doctor |
2069 |
35.4 |
37.3 |
| With_doctor |
1349 |
23.1 |
24.3 |
| Doctor_made |
493 |
8.4 |
8.9 |
| Dont_know_or_remember |
0 |
0.0 |
0.0 |
| NA |
298 |
5.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E9: The most important factors of tx
- E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
- E9_1: 1=Best chance for cure of my cancer
- E9_2: 1=Minimize side effects related to sexual function
- E9_3: 1=Minimize side effects related to urinary function
- E9_4: 1=Minimize side effects related to bowel function
- E9_5: 1=Minimize financial cost
- E9_6: 1=Amount of time and travel required to receive treatments
- E9_7: 1=Length of recovery time
- E9_8: 1=Amount of time away from work
- E9_9: 1=Burden on family members
- E9_10: 1=Reduce worry and concern about cancer
e9_1 <- as.factor(d[,"e9_1"])
levels(e9_1) <- list(Best_for_cure="1")
new.d <- data.frame(new.d, e9_1)
new.d <- apply_labels(new.d, e9_1 = "Best for cure")
temp.d <- data.frame (new.d, e9_1)
result<-questionr::freq(temp.d$e9_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
| Best_for_cure |
5010 |
85.8 |
100 |
| NA |
829 |
14.2 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_2 <- as.factor(d[,"e9_2"])
levels(e9_2) <- list(side_effects_sexual="1")
new.d <- data.frame(new.d, e9_2)
new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
temp.d <- data.frame (new.d, e9_2)
result<-questionr::freq(temp.d$e9_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
| side_effects_sexual |
1754 |
30 |
100 |
| NA |
4085 |
70 |
NA |
| Total |
5839 |
100 |
100 |
e9_3 <- as.factor(d[,"e9_3"])
levels(e9_3) <- list(side_effects_urinary="1")
new.d <- data.frame(new.d, e9_3)
new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
temp.d <- data.frame (new.d, e9_3)
result<-questionr::freq(temp.d$e9_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
| side_effects_urinary |
1548 |
26.5 |
100 |
| NA |
4291 |
73.5 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_4 <- as.factor(d[,"e9_4"])
levels(e9_4) <- list(side_effects_bowel="1")
new.d <- data.frame(new.d, e9_4)
new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
temp.d <- data.frame (new.d, e9_4)
result<-questionr::freq(temp.d$e9_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
| side_effects_bowel |
770 |
13.2 |
100 |
| NA |
5069 |
86.8 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_5 <- as.factor(d[,"e9_5"])
levels(e9_5) <- list(financial_cost="1")
new.d <- data.frame(new.d, e9_5)
new.d <- apply_labels(new.d, e9_5 = "financial cost")
temp.d <- data.frame (new.d, e9_5)
result<-questionr::freq(temp.d$e9_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
| financial_cost |
348 |
6 |
100 |
| NA |
5491 |
94 |
NA |
| Total |
5839 |
100 |
100 |
e9_6 <- as.factor(d[,"e9_6"])
levels(e9_6) <- list(time_and_travel="1")
new.d <- data.frame(new.d, e9_6)
new.d <- apply_labels(new.d, e9_6 = "time and travel")
temp.d <- data.frame (new.d, e9_6)
result<-questionr::freq(temp.d$e9_6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
| time_and_travel |
652 |
11.2 |
100 |
| NA |
5187 |
88.8 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_7 <- as.factor(d[,"e9_7"])
levels(e9_7) <- list(recovery_time="1")
new.d <- data.frame(new.d, e9_7)
new.d <- apply_labels(new.d, e9_7 = "recovery time")
temp.d <- data.frame (new.d, e9_7)
result<-questionr::freq(temp.d$e9_7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
| recovery_time |
1134 |
19.4 |
100 |
| NA |
4705 |
80.6 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_8 <- as.factor(d[,"e9_8"])
levels(e9_8) <- list(time_away_from_work="1")
new.d <- data.frame(new.d, e9_8)
new.d <- apply_labels(new.d, e9_8 = "time away from work")
temp.d <- data.frame (new.d, e9_8)
result<-questionr::freq(temp.d$e9_8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
| time_away_from_work |
475 |
8.1 |
100 |
| NA |
5364 |
91.9 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_9 <- as.factor(d[,"e9_9"])
levels(e9_9) <- list(family_burden="1")
new.d <- data.frame(new.d, e9_9)
new.d <- apply_labels(new.d, e9_9 = "family burden")
temp.d <- data.frame (new.d, e9_9)
result<-questionr::freq(temp.d$e9_9,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
| family_burden |
856 |
14.7 |
100 |
| NA |
4983 |
85.3 |
NA |
| Total |
5839 |
100.0 |
100 |
e9_10 <- as.factor(d[,"e9_10"])
levels(e9_10) <- list(Reduce_worry_concern="1")
new.d <- data.frame(new.d, e9_10)
new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
temp.d <- data.frame (new.d, e9_10)
result<-questionr::freq(temp.d$e9_10,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
| Reduce_worry_concern |
2519 |
43.1 |
100 |
| NA |
3320 |
56.9 |
NA |
| Total |
5839 |
100.0 |
100 |
E10: Recieved treatment
- E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
- E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
- E10_2: 1=Active Surveillance or watchful waiting
- E10_3: 1=Prostate surgery (prostatectomy)
- E10_4: 1=Radiation to the prostate
- E10_5: 1=Hormonal treatments
- E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
- E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
- E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
e10_1 <- as.factor(d[,"e10_1"])
levels(e10_1) <- list(no_treatment="1")
new.d <- data.frame(new.d, e10_1)
new.d <- apply_labels(new.d, e10_1 = "no treatment")
temp.d <- data.frame (new.d, e10_1)
result<-questionr::freq(temp.d$e10_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
| no_treatment |
327 |
5.6 |
100 |
| NA |
5512 |
94.4 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_2 <- as.factor(d[,"e10_2"])
levels(e10_2) <- list(Active_Surveillance="1")
new.d <- data.frame(new.d, e10_2)
new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
temp.d <- data.frame (new.d, e10_2)
result<-questionr::freq(temp.d$e10_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
| Active_Surveillance |
964 |
16.5 |
100 |
| NA |
4875 |
83.5 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_3 <- as.factor(d[,"e10_3"])
levels(e10_3) <- list(prostatectomy="1")
new.d <- data.frame(new.d, e10_3)
new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
temp.d <- data.frame (new.d, e10_3)
result<-questionr::freq(temp.d$e10_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
| prostatectomy |
1927 |
33 |
100 |
| NA |
3912 |
67 |
NA |
| Total |
5839 |
100 |
100 |
e10_4 <- as.factor(d[,"e10_4"])
levels(e10_4) <- list(Radiation="1")
new.d <- data.frame(new.d, e10_4)
new.d <- apply_labels(new.d, e10_4 = "Radiation")
temp.d <- data.frame (new.d, e10_4)
result<-questionr::freq(temp.d$e10_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
| Radiation |
2313 |
39.6 |
100 |
| NA |
3526 |
60.4 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_5 <- as.factor(d[,"e10_5"])
levels(e10_5) <- list(Hormonal_treatments="1")
new.d <- data.frame(new.d, e10_5)
new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
temp.d <- data.frame (new.d, e10_5)
result<-questionr::freq(temp.d$e10_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
| Hormonal_treatments |
792 |
13.6 |
100 |
| NA |
5047 |
86.4 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_6 <- as.factor(d[,"e10_6"])
levels(e10_6) <- list(Provenge_immunotherapy="1")
new.d <- data.frame(new.d, e10_6)
new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
temp.d <- data.frame (new.d, e10_6)
result<-questionr::freq(temp.d$e10_6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
| Provenge_immunotherapy |
59 |
1 |
100 |
| NA |
5780 |
99 |
NA |
| Total |
5839 |
100 |
100 |
e10_7 <- as.factor(d[,"e10_7"])
levels(e10_7) <- list(Chemotherapy="1")
new.d <- data.frame(new.d, e10_7)
new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
temp.d <- data.frame (new.d, e10_7)
result<-questionr::freq(temp.d$e10_7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
| Chemotherapy |
184 |
3.2 |
100 |
| NA |
5655 |
96.8 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_8 <- as.factor(d[,"e10_8"])
levels(e10_8) <- list(Other="1")
new.d <- data.frame(new.d, e10_8)
new.d <- apply_labels(new.d, e10_8 = "Other")
temp.d <- data.frame (new.d, e10_8)
result<-questionr::freq(temp.d$e10_8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
| Other |
274 |
4.7 |
100 |
| NA |
5565 |
95.3 |
NA |
| Total |
5839 |
100.0 |
100 |
E10-3 Prostatectomy
- E10_3. Prostate surgery (prostatectomy), indicate which type(s):
- E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
- E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
- E10_3_3: 1=Had surgery but unsure of type
e10_3_1 <- as.factor(d[,"e10_3_1"])
levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
new.d <- data.frame(new.d, e10_3_1)
new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
temp.d <- data.frame (new.d, e10_3_1)
result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
| Robotic_laproscopic_surgery |
1979 |
33.9 |
100 |
| NA |
3860 |
66.1 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_3_2 <- as.factor(d[,"e10_3_2"])
levels(e10_3_2) <- list(Open_surgical_removal="1")
new.d <- data.frame(new.d, e10_3_2)
new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
temp.d <- data.frame (new.d, e10_3_2)
result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
| Open_surgical_removal |
302 |
5.2 |
100 |
| NA |
5537 |
94.8 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_3_3 <- as.factor(d[,"e10_3_3"])
levels(e10_3_3) <- list(unsure_of_type="1")
new.d <- data.frame(new.d, e10_3_3)
new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
temp.d <- data.frame (new.d, e10_3_3)
result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
| unsure_of_type |
347 |
5.9 |
100 |
| NA |
5492 |
94.1 |
NA |
| Total |
5839 |
100.0 |
100 |
E10-4 Radiation
- E10_4. Radiation to the prostate, indicate which type(s):
- E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
- E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
- E10_4_3: 1=Other types of radiation therapy, or unsure of what type
e10_4_1 <- as.factor(d[,"e10_4_1"])
levels(e10_4_1) <- list(External_beam_radiation="1")
new.d <- data.frame(new.d, e10_4_1)
new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
temp.d <- data.frame (new.d, e10_4_1)
result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
| External_beam_radiation |
2147 |
36.8 |
100 |
| NA |
3692 |
63.2 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_4_2 <- as.factor(d[,"e10_4_2"])
levels(e10_4_2) <- list(brachytherapy="1")
new.d <- data.frame(new.d, e10_4_2)
new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
temp.d <- data.frame (new.d, e10_4_2)
result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
| brachytherapy |
911 |
15.6 |
100 |
| NA |
4928 |
84.4 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_4_3 <- as.factor(d[,"e10_4_3"])
levels(e10_4_3) <- list(Other_types="1")
new.d <- data.frame(new.d, e10_4_3)
new.d <- apply_labels(new.d, e10_4_3 = "Other types")
temp.d <- data.frame (new.d, e10_4_3)
result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
| Other_types |
326 |
5.6 |
100 |
| NA |
5513 |
94.4 |
NA |
| Total |
5839 |
100.0 |
100 |
E10-5 Hormonal treatments
- E10_5. Hormonal treatments, indicate which type(s):
- E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
- E10_5_2: 1= Surgical removal of testicles (orchiectomy)
- E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
- E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
- E10_5_5: 1=Had hormone treatment, but unsure of type
e10_5_1 <- as.factor(d[,"e10_5_1"])
levels(e10_5_1) <- list(Hormone_shots="1")
new.d <- data.frame(new.d, e10_5_1)
new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
temp.d <- data.frame (new.d, e10_5_1)
result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
| Hormone_shots |
1106 |
18.9 |
100 |
| NA |
4733 |
81.1 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_5_2 <- as.factor(d[,"e10_5_2"])
levels(e10_5_2) <- list(orchiectomy="1")
new.d <- data.frame(new.d, e10_5_2)
new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
temp.d <- data.frame (new.d, e10_5_2)
result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
| orchiectomy |
64 |
1.1 |
100 |
| NA |
5775 |
98.9 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_5_3 <- as.factor(d[,"e10_5_3"])
levels(e10_5_3) <- list(Casodex_Eulexin="1")
new.d <- data.frame(new.d, e10_5_3)
new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
temp.d <- data.frame (new.d, e10_5_3)
result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
| Casodex_Eulexin |
128 |
2.2 |
100 |
| NA |
5711 |
97.8 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_5_4 <- as.factor(d[,"e10_5_4"])
levels(e10_5_4) <- list(Zytiga_Xtandi="1")
new.d <- data.frame(new.d, e10_5_4)
new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
temp.d <- data.frame (new.d, e10_5_4)
result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
| Zytiga_Xtandi |
112 |
1.9 |
100 |
| NA |
5727 |
98.1 |
NA |
| Total |
5839 |
100.0 |
100 |
e10_5_5 <- as.factor(d[,"e10_5_5"])
levels(e10_5_5) <- list(unsure_type="1")
new.d <- data.frame(new.d, e10_5_5)
new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
temp.d <- data.frame (new.d, e10_5_5)
result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
| unsure_type |
286 |
4.9 |
100 |
| NA |
5553 |
95.1 |
NA |
| Total |
5839 |
100.0 |
100 |
E11: Treatment decision
- E11. Your treatment decision: How true is each of the following statements for you?
- I had all the information I needed when a treatment was chosen for my prostate cancer
- My doctors told me the whole story about the effects of treatment
- I knew the right questions to ask my doctor
- I had enough time to make a decision about my treatment
- I am satisfied with the choices I made in treating my prostate cancer
- I would recommend the treatment I had to a close relative or friend
- 1=Not at all
- 2=A little bit
- 3=Somewhat
- 4=Quite a bit
- 5=Very much
e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
levels(e11a) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11a)
new.d <- apply_labels(new.d, e11a = "all info")
temp.d <- data.frame (new.d, e11a)
result<-questionr::freq(temp.d$e11a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
| Not_at_all |
181 |
3.1 |
3.2 |
| A_little_bit |
204 |
3.5 |
3.6 |
| Somewhat |
648 |
11.1 |
11.6 |
| Quite_a_bit |
1483 |
25.4 |
26.5 |
| Very_much |
3078 |
52.7 |
55.0 |
| NA |
245 |
4.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
levels(e11b) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11b)
new.d <- apply_labels(new.d, e11b = "be told about effects")
temp.d <- data.frame (new.d, e11b)
result<-questionr::freq(temp.d$e11b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
| Not_at_all |
153 |
2.6 |
2.7 |
| A_little_bit |
216 |
3.7 |
3.9 |
| Somewhat |
662 |
11.3 |
11.8 |
| Quite_a_bit |
1376 |
23.6 |
24.5 |
| Very_much |
3201 |
54.8 |
57.1 |
| NA |
231 |
4.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
e11c <- as.factor(d[,"e11c"])
# Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
levels(e11c) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11c)
new.d <- apply_labels(new.d, e11c = "right questions to ask")
temp.d <- data.frame (new.d, e11c)
result<-questionr::freq(temp.d$e11c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
| Not_at_all |
554 |
9.5 |
9.9 |
| A_little_bit |
656 |
11.2 |
11.7 |
| Somewhat |
1797 |
30.8 |
32.2 |
| Quite_a_bit |
1162 |
19.9 |
20.8 |
| Very_much |
1419 |
24.3 |
25.4 |
| NA |
251 |
4.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
e11d <- as.factor(d[,"e11d"])
# Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
levels(e11d) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11d)
new.d <- apply_labels(new.d, e11d = "enough time to decide")
temp.d <- data.frame (new.d, e11d)
result<-questionr::freq(temp.d$e11d,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
| Not_at_all |
165 |
2.8 |
3.0 |
| A_little_bit |
224 |
3.8 |
4.0 |
| Somewhat |
845 |
14.5 |
15.1 |
| Quite_a_bit |
1361 |
23.3 |
24.4 |
| Very_much |
2994 |
51.3 |
53.6 |
| NA |
250 |
4.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
e11e <- as.factor(d[,"e11e"])
# Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
levels(e11e) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11e)
new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
temp.d <- data.frame (new.d, e11e)
result<-questionr::freq(temp.d$e11e,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
| Not_at_all |
213 |
3.6 |
3.8 |
| A_little_bit |
225 |
3.9 |
4.0 |
| Somewhat |
689 |
11.8 |
12.3 |
| Quite_a_bit |
867 |
14.8 |
15.5 |
| Very_much |
3611 |
61.8 |
64.4 |
| NA |
234 |
4.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
e11f <- as.factor(d[,"e11f"])
# Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
levels(e11f) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11f)
new.d <- apply_labels(new.d, e11f = "would recommend")
temp.d <- data.frame (new.d, e11f)
result<-questionr::freq(temp.d$e11f,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
| Not_at_all |
341 |
5.8 |
6.1 |
| A_little_bit |
227 |
3.9 |
4.1 |
| Somewhat |
803 |
13.8 |
14.4 |
| Quite_a_bit |
767 |
13.1 |
13.8 |
| Very_much |
3424 |
58.6 |
61.6 |
| NA |
277 |
4.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E12: Instructions from doctors or nurses
- E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure
e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
levels(e12) <- list(No="1",
Yes="2",
Dont_know="88")
e12 <- ordered(e12, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e12)
new.d <- apply_labels(new.d, e12 = "received instructions")
temp.d <- data.frame (new.d, e12)
result<-questionr::freq(temp.d$e12,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e12")
e12
| No |
697 |
11.9 |
12.3 |
| Yes |
4662 |
79.8 |
82.5 |
| Dont_know |
295 |
5.1 |
5.2 |
| NA |
185 |
3.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E13: # of PSA blood test
- E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
- 0=None
- 1=1
- 2=2
- 3=3
- 4=4 or more
- 88=Don’t know/not sure
e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
levels(e13) <- list(None="0",
One="1",
Two="2",
Three="3",
Four_more="4",
Dont_know="88")
e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
new.d <- data.frame(new.d, e13)
new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
temp.d <- data.frame (new.d, e13)
result<-questionr::freq(temp.d$e13,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e13")
e13
| None |
133 |
2.3 |
2.3 |
| One |
146 |
2.5 |
2.6 |
| Two |
302 |
5.2 |
5.3 |
| Three |
549 |
9.4 |
9.7 |
| Four_more |
4020 |
68.8 |
71.0 |
| Dont_know |
510 |
8.7 |
9.0 |
| NA |
179 |
3.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E14: Be told PSA was rising
- E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure
e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
levels(e14) <- list(No="1",
Yes="2",
Dont_know="88")
e14 <- ordered(e14, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e14)
new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
temp.d <- data.frame (new.d, e14)
result<-questionr::freq(temp.d$e14,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e14")
e14
| No |
4076 |
69.8 |
72.0 |
| Yes |
1240 |
21.2 |
21.9 |
| Dont_know |
345 |
5.9 |
6.1 |
| NA |
178 |
3.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
E15: Recurred or got worse
- E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure
e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
levels(e15) <- list(No="1",
Yes="2",
Dont_know="88")
e15 <- ordered(e15, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e15)
new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
temp.d <- data.frame (new.d, e15)
result<-questionr::freq(temp.d$e15,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e15")
e15
| No |
4919 |
84.2 |
86.8 |
| Yes |
502 |
8.6 |
8.9 |
| Dont_know |
249 |
4.3 |
4.4 |
| NA |
169 |
2.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
F1: Height
f1cm <- d[,"f1cm"]
new.d <- data.frame(new.d, f1cm)
new.d <- apply_labels(new.d, f1cm = "height in cm")
temp.d <- data.frame (new.d, f1cm)
result<-questionr::freq(temp.d$f1cm,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
| 0 |
10 |
0.2 |
8.8 |
| 0 0 |
1 |
0.0 |
0.9 |
| 0 9 |
1 |
0.0 |
0.9 |
| 1 |
13 |
0.2 |
11.4 |
| 10 |
4 |
0.1 |
3.5 |
| 11 |
5 |
0.1 |
4.4 |
| 111 |
1 |
0.0 |
0.9 |
| 12 |
1 |
0.0 |
0.9 |
| 135 |
1 |
0.0 |
0.9 |
| 148 |
1 |
0.0 |
0.9 |
| 151 |
1 |
0.0 |
0.9 |
| 152 |
1 |
0.0 |
0.9 |
| 154 |
1 |
0.0 |
0.9 |
| 164 |
1 |
0.0 |
0.9 |
| 165 |
2 |
0.0 |
1.8 |
| 17 |
1 |
0.0 |
0.9 |
| 170 |
1 |
0.0 |
0.9 |
| 173 |
1 |
0.0 |
0.9 |
| 175 |
1 |
0.0 |
0.9 |
| 177 |
1 |
0.0 |
0.9 |
| 178 |
2 |
0.0 |
1.8 |
| 180 |
2 |
0.0 |
1.8 |
| 181 |
2 |
0.0 |
1.8 |
| 185 |
2 |
0.0 |
1.8 |
| 188 |
1 |
0.0 |
0.9 |
| 190 |
4 |
0.1 |
3.5 |
| 2 |
7 |
0.1 |
6.1 |
| 2** |
1 |
0.0 |
0.9 |
| 200 |
1 |
0.0 |
0.9 |
| 21 |
1 |
0.0 |
0.9 |
| 211 |
1 |
0.0 |
0.9 |
| 215 |
1 |
0.0 |
0.9 |
| 225 |
1 |
0.0 |
0.9 |
| 228 |
1 |
0.0 |
0.9 |
| 230 |
1 |
0.0 |
0.9 |
| 234 |
1 |
0.0 |
0.9 |
| 245 |
1 |
0.0 |
0.9 |
| 247 |
1 |
0.0 |
0.9 |
| 250 |
1 |
0.0 |
0.9 |
| 255 |
1 |
0.0 |
0.9 |
| 265 |
1 |
0.0 |
0.9 |
| 280 |
1 |
0.0 |
0.9 |
| 290 |
1 |
0.0 |
0.9 |
| 3 |
2 |
0.0 |
1.8 |
| 44 |
1 |
0.0 |
0.9 |
| 47 |
1 |
0.0 |
0.9 |
| 5 |
6 |
0.1 |
5.3 |
| 6 |
2 |
0.0 |
1.8 |
| 7 |
4 |
0.1 |
3.5 |
| 72 |
1 |
0.0 |
0.9 |
| 78 |
1 |
0.0 |
0.9 |
| 8 |
3 |
0.1 |
2.6 |
| 9 |
7 |
0.1 |
6.1 |
| 91 |
1 |
0.0 |
0.9 |
| NA |
5725 |
98.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
F2: Weight
- F2. How much do you current weight?
f2lbs <- d[,"f2lbs"]
new.d <- data.frame(new.d, f2lbs)
new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
temp.d <- data.frame (new.d, f2lbs)
result<-questionr::freq(temp.d$f2lbs,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
| * |
4 |
0.1 |
0.1 |
| * 0 |
1 |
0.0 |
0.0 |
| * 3 |
1 |
0.0 |
0.0 |
| * 4 |
1 |
0.0 |
0.0 |
| * 5 |
2 |
0.0 |
0.0 |
| * 9 |
1 |
0.0 |
0.0 |
| *0 |
2 |
0.0 |
0.0 |
| *2 |
1 |
0.0 |
0.0 |
| *35 |
1 |
0.0 |
0.0 |
| *4 |
2 |
0.0 |
0.0 |
| *5 |
2 |
0.0 |
0.0 |
| *6 |
1 |
0.0 |
0.0 |
| *8 |
1 |
0.0 |
0.0 |
| *99 |
1 |
0.0 |
0.0 |
| 0 |
2 |
0.0 |
0.0 |
| 0* |
1 |
0.0 |
0.0 |
| 1 |
12 |
0.2 |
0.2 |
| 1 * |
4 |
0.1 |
0.1 |
| 1 8 |
1 |
0.0 |
0.0 |
| 1* |
5 |
0.1 |
0.1 |
| 100 |
1 |
0.0 |
0.0 |
| 105 |
1 |
0.0 |
0.0 |
| 106 |
1 |
0.0 |
0.0 |
| 108 |
1 |
0.0 |
0.0 |
| 110 |
2 |
0.0 |
0.0 |
| 111 |
2 |
0.0 |
0.0 |
| 112 |
1 |
0.0 |
0.0 |
| 114 |
1 |
0.0 |
0.0 |
| 115 |
4 |
0.1 |
0.1 |
| 117 |
1 |
0.0 |
0.0 |
| 118 |
3 |
0.1 |
0.1 |
| 119 |
2 |
0.0 |
0.0 |
| 12 |
1 |
0.0 |
0.0 |
| 120 |
13 |
0.2 |
0.2 |
| 121 |
1 |
0.0 |
0.0 |
| 122 |
3 |
0.1 |
0.1 |
| 124 |
4 |
0.1 |
0.1 |
| 125 |
10 |
0.2 |
0.2 |
| 126 |
1 |
0.0 |
0.0 |
| 127 |
3 |
0.1 |
0.1 |
| 130 |
21 |
0.4 |
0.4 |
| 132 |
6 |
0.1 |
0.1 |
| 133 |
3 |
0.1 |
0.1 |
| 134 |
1 |
0.0 |
0.0 |
| 135 |
21 |
0.4 |
0.4 |
| 136 |
2 |
0.0 |
0.0 |
| 137 |
9 |
0.2 |
0.2 |
| 138 |
8 |
0.1 |
0.2 |
| 139 |
8 |
0.1 |
0.2 |
| 14* |
2 |
0.0 |
0.0 |
| 140 |
38 |
0.7 |
0.7 |
| 141 |
4 |
0.1 |
0.1 |
| 142 |
6 |
0.1 |
0.1 |
| 143 |
6 |
0.1 |
0.1 |
| 144 |
1 |
0.0 |
0.0 |
| 145 |
29 |
0.5 |
0.6 |
| 146 |
8 |
0.1 |
0.2 |
| 147 |
8 |
0.1 |
0.2 |
| 148 |
14 |
0.2 |
0.3 |
| 149 |
3 |
0.1 |
0.1 |
| 150 |
63 |
1.1 |
1.2 |
| 151 |
5 |
0.1 |
0.1 |
| 152 |
13 |
0.2 |
0.2 |
| 153 |
13 |
0.2 |
0.2 |
| 154 |
14 |
0.2 |
0.3 |
| 155 |
59 |
1.0 |
1.1 |
| 156 |
19 |
0.3 |
0.4 |
| 157 |
11 |
0.2 |
0.2 |
| 158 |
20 |
0.3 |
0.4 |
| 159 |
11 |
0.2 |
0.2 |
| 16 |
1 |
0.0 |
0.0 |
| 160 |
94 |
1.6 |
1.8 |
| 161 |
10 |
0.2 |
0.2 |
| 162 |
28 |
0.5 |
0.5 |
| 163 |
20 |
0.3 |
0.4 |
| 164 |
14 |
0.2 |
0.3 |
| 165 |
86 |
1.5 |
1.6 |
| 166 |
11 |
0.2 |
0.2 |
| 167 |
32 |
0.5 |
0.6 |
| 168 |
44 |
0.8 |
0.8 |
| 169 |
19 |
0.3 |
0.4 |
| 17 |
1 |
0.0 |
0.0 |
| 170 |
108 |
1.8 |
2.1 |
| 171 |
13 |
0.2 |
0.2 |
| 172 |
36 |
0.6 |
0.7 |
| 173 |
28 |
0.5 |
0.5 |
| 174 |
28 |
0.5 |
0.5 |
| 175 |
127 |
2.2 |
2.4 |
| 176 |
32 |
0.5 |
0.6 |
| 177 |
29 |
0.5 |
0.6 |
| 178 |
52 |
0.9 |
1.0 |
| 179 |
15 |
0.3 |
0.3 |
| 18 |
2 |
0.0 |
0.0 |
| 18* |
1 |
0.0 |
0.0 |
| 180 |
146 |
2.5 |
2.8 |
| 181 |
15 |
0.3 |
0.3 |
| 182 |
43 |
0.7 |
0.8 |
| 183 |
35 |
0.6 |
0.7 |
| 184 |
37 |
0.6 |
0.7 |
| 185 |
136 |
2.3 |
2.6 |
| 186 |
27 |
0.5 |
0.5 |
| 187 |
34 |
0.6 |
0.6 |
| 188 |
49 |
0.8 |
0.9 |
| 189 |
41 |
0.7 |
0.8 |
| 190 |
150 |
2.6 |
2.9 |
| 191 |
12 |
0.2 |
0.2 |
| 192 |
44 |
0.8 |
0.8 |
| 193 |
29 |
0.5 |
0.6 |
| 194 |
17 |
0.3 |
0.3 |
| 195 |
121 |
2.1 |
2.3 |
| 196 |
32 |
0.5 |
0.6 |
| 197 |
41 |
0.7 |
0.8 |
| 198 |
56 |
1.0 |
1.1 |
| 199 |
26 |
0.4 |
0.5 |
| 2 |
18 |
0.3 |
0.3 |
| 2 * |
1 |
0.0 |
0.0 |
| 2 1 |
1 |
0.0 |
0.0 |
| 2 2 |
1 |
0.0 |
0.0 |
| 2 6 |
1 |
0.0 |
0.0 |
| 2* |
9 |
0.2 |
0.2 |
| 200 |
163 |
2.8 |
3.1 |
| 201 |
10 |
0.2 |
0.2 |
| 202 |
31 |
0.5 |
0.6 |
| 203 |
21 |
0.4 |
0.4 |
| 204 |
35 |
0.6 |
0.7 |
| 205 |
122 |
2.1 |
2.3 |
| 206 |
18 |
0.3 |
0.3 |
| 207 |
32 |
0.5 |
0.6 |
| 208 |
31 |
0.5 |
0.6 |
| 209 |
23 |
0.4 |
0.4 |
| 21 |
1 |
0.0 |
0.0 |
| 210 |
163 |
2.8 |
3.1 |
| 211 |
15 |
0.3 |
0.3 |
| 212 |
49 |
0.8 |
0.9 |
| 213 |
15 |
0.3 |
0.3 |
| 214 |
39 |
0.7 |
0.7 |
| 215 |
135 |
2.3 |
2.6 |
| 216 |
14 |
0.2 |
0.3 |
| 217 |
17 |
0.3 |
0.3 |
| 218 |
43 |
0.7 |
0.8 |
| 219 |
13 |
0.2 |
0.2 |
| 220 |
146 |
2.5 |
2.8 |
| 221 |
15 |
0.3 |
0.3 |
| 222 |
27 |
0.5 |
0.5 |
| 223 |
27 |
0.5 |
0.5 |
| 224 |
18 |
0.3 |
0.3 |
| 225 |
83 |
1.4 |
1.6 |
| 226 |
20 |
0.3 |
0.4 |
| 227 |
17 |
0.3 |
0.3 |
| 228 |
19 |
0.3 |
0.4 |
| 229 |
12 |
0.2 |
0.2 |
| 23 |
1 |
0.0 |
0.0 |
| 230 |
127 |
2.2 |
2.4 |
| 231 |
5 |
0.1 |
0.1 |
| 232 |
22 |
0.4 |
0.4 |
| 233 |
21 |
0.4 |
0.4 |
| 234 |
25 |
0.4 |
0.5 |
| 235 |
83 |
1.4 |
1.6 |
| 236 |
11 |
0.2 |
0.2 |
| 237 |
17 |
0.3 |
0.3 |
| 238 |
22 |
0.4 |
0.4 |
| 239 |
4 |
0.1 |
0.1 |
| 240 |
102 |
1.7 |
1.9 |
| 241 |
10 |
0.2 |
0.2 |
| 242 |
25 |
0.4 |
0.5 |
| 243 |
12 |
0.2 |
0.2 |
| 244 |
10 |
0.2 |
0.2 |
| 245 |
63 |
1.1 |
1.2 |
| 246 |
14 |
0.2 |
0.3 |
| 247 |
17 |
0.3 |
0.3 |
| 248 |
12 |
0.2 |
0.2 |
| 249 |
14 |
0.2 |
0.3 |
| 250 |
92 |
1.6 |
1.8 |
| 251 |
6 |
0.1 |
0.1 |
| 252 |
11 |
0.2 |
0.2 |
| 253 |
6 |
0.1 |
0.1 |
| 254 |
15 |
0.3 |
0.3 |
| 255 |
30 |
0.5 |
0.6 |
| 256 |
6 |
0.1 |
0.1 |
| 257 |
6 |
0.1 |
0.1 |
| 258 |
5 |
0.1 |
0.1 |
| 259 |
8 |
0.1 |
0.2 |
| 260 |
66 |
1.1 |
1.3 |
| 261 |
4 |
0.1 |
0.1 |
| 262 |
15 |
0.3 |
0.3 |
| 263 |
6 |
0.1 |
0.1 |
| 264 |
7 |
0.1 |
0.1 |
| 265 |
40 |
0.7 |
0.8 |
| 266 |
9 |
0.2 |
0.2 |
| 267 |
7 |
0.1 |
0.1 |
| 268 |
6 |
0.1 |
0.1 |
| 269 |
3 |
0.1 |
0.1 |
| 270 |
50 |
0.9 |
1.0 |
| 271 |
5 |
0.1 |
0.1 |
| 272 |
6 |
0.1 |
0.1 |
| 273 |
4 |
0.1 |
0.1 |
| 274 |
4 |
0.1 |
0.1 |
| 275 |
19 |
0.3 |
0.4 |
| 276 |
4 |
0.1 |
0.1 |
| 277 |
7 |
0.1 |
0.1 |
| 278 |
5 |
0.1 |
0.1 |
| 279 |
4 |
0.1 |
0.1 |
| 280 |
37 |
0.6 |
0.7 |
| 281 |
3 |
0.1 |
0.1 |
| 282 |
2 |
0.0 |
0.0 |
| 284 |
2 |
0.0 |
0.0 |
| 285 |
16 |
0.3 |
0.3 |
| 286 |
2 |
0.0 |
0.0 |
| 287 |
3 |
0.1 |
0.1 |
| 288 |
3 |
0.1 |
0.1 |
| 289 |
8 |
0.1 |
0.2 |
| 29 |
1 |
0.0 |
0.0 |
| 290 |
14 |
0.2 |
0.3 |
| 291 |
1 |
0.0 |
0.0 |
| 292 |
4 |
0.1 |
0.1 |
| 294 |
3 |
0.1 |
0.1 |
| 295 |
13 |
0.2 |
0.2 |
| 296 |
2 |
0.0 |
0.0 |
| 297 |
6 |
0.1 |
0.1 |
| 298 |
9 |
0.2 |
0.2 |
| 3 |
1 |
0.0 |
0.0 |
| 300 |
24 |
0.4 |
0.5 |
| 302 |
2 |
0.0 |
0.0 |
| 303 |
1 |
0.0 |
0.0 |
| 304 |
1 |
0.0 |
0.0 |
| 305 |
4 |
0.1 |
0.1 |
| 306 |
1 |
0.0 |
0.0 |
| 307 |
3 |
0.1 |
0.1 |
| 308 |
1 |
0.0 |
0.0 |
| 309 |
3 |
0.1 |
0.1 |
| 310 |
13 |
0.2 |
0.2 |
| 311 |
2 |
0.0 |
0.0 |
| 312 |
2 |
0.0 |
0.0 |
| 314 |
1 |
0.0 |
0.0 |
| 315 |
6 |
0.1 |
0.1 |
| 316 |
1 |
0.0 |
0.0 |
| 317 |
3 |
0.1 |
0.1 |
| 318 |
1 |
0.0 |
0.0 |
| 319 |
1 |
0.0 |
0.0 |
| 320 |
8 |
0.1 |
0.2 |
| 321 |
3 |
0.1 |
0.1 |
| 323 |
1 |
0.0 |
0.0 |
| 324 |
2 |
0.0 |
0.0 |
| 325 |
6 |
0.1 |
0.1 |
| 326 |
2 |
0.0 |
0.0 |
| 330 |
11 |
0.2 |
0.2 |
| 333 |
1 |
0.0 |
0.0 |
| 334 |
1 |
0.0 |
0.0 |
| 335 |
4 |
0.1 |
0.1 |
| 340 |
4 |
0.1 |
0.1 |
| 344 |
1 |
0.0 |
0.0 |
| 345 |
2 |
0.0 |
0.0 |
| 350 |
5 |
0.1 |
0.1 |
| 354 |
1 |
0.0 |
0.0 |
| 355 |
2 |
0.0 |
0.0 |
| 358 |
1 |
0.0 |
0.0 |
| 360 |
1 |
0.0 |
0.0 |
| 361 |
1 |
0.0 |
0.0 |
| 362 |
1 |
0.0 |
0.0 |
| 365 |
2 |
0.0 |
0.0 |
| 370 |
1 |
0.0 |
0.0 |
| 375 |
1 |
0.0 |
0.0 |
| 376 |
1 |
0.0 |
0.0 |
| 385 |
1 |
0.0 |
0.0 |
| 389 |
1 |
0.0 |
0.0 |
| 39 |
1 |
0.0 |
0.0 |
| 395 |
1 |
0.0 |
0.0 |
| 397 |
1 |
0.0 |
0.0 |
| 400 |
3 |
0.1 |
0.1 |
| 410 |
1 |
0.0 |
0.0 |
| 415 |
1 |
0.0 |
0.0 |
| 416 |
1 |
0.0 |
0.0 |
| 424 |
1 |
0.0 |
0.0 |
| 430 |
1 |
0.0 |
0.0 |
| 440 |
1 |
0.0 |
0.0 |
| 45 |
1 |
0.0 |
0.0 |
| 50 |
1 |
0.0 |
0.0 |
| 53 |
1 |
0.0 |
0.0 |
| 60 |
1 |
0.0 |
0.0 |
| 65 |
4 |
0.1 |
0.1 |
| 68 |
3 |
0.1 |
0.1 |
| 7 |
1 |
0.0 |
0.0 |
| 71 |
1 |
0.0 |
0.0 |
| 72 |
2 |
0.0 |
0.0 |
| 74 |
2 |
0.0 |
0.0 |
| 75 |
1 |
0.0 |
0.0 |
| 76 |
1 |
0.0 |
0.0 |
| 78 |
1 |
0.0 |
0.0 |
| 80 |
5 |
0.1 |
0.1 |
| 81 |
1 |
0.0 |
0.0 |
| 82 |
1 |
0.0 |
0.0 |
| 84 |
1 |
0.0 |
0.0 |
| 89 |
1 |
0.0 |
0.0 |
| 90 |
3 |
0.1 |
0.1 |
| 91 |
1 |
0.0 |
0.0 |
| 92 |
1 |
0.0 |
0.0 |
| 94 |
1 |
0.0 |
0.0 |
| 97 |
2 |
0.0 |
0.0 |
| 98 |
1 |
0.0 |
0.0 |
| NA |
596 |
10.2 |
NA |
| Total |
5839 |
100.0 |
100.0 |
f2kgs <- d[,"f2kgs"]
new.d <- data.frame(new.d, f2kgs)
new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
temp.d <- data.frame (new.d, f2kgs)
result<-questionr::freq(temp.d$f2kgs,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
| * |
1 |
0.0 |
1.2 |
| 0 |
16 |
0.3 |
19.8 |
| 1 |
3 |
0.1 |
3.7 |
| 1 7 |
1 |
0.0 |
1.2 |
| 10 |
1 |
0.0 |
1.2 |
| 110 |
1 |
0.0 |
1.2 |
| 111 |
3 |
0.1 |
3.7 |
| 112 |
1 |
0.0 |
1.2 |
| 113 |
1 |
0.0 |
1.2 |
| 114 |
1 |
0.0 |
1.2 |
| 137 |
1 |
0.0 |
1.2 |
| 155 |
1 |
0.0 |
1.2 |
| 175 |
1 |
0.0 |
1.2 |
| 198 |
1 |
0.0 |
1.2 |
| 2 |
4 |
0.1 |
4.9 |
| 20 |
1 |
0.0 |
1.2 |
| 22 |
1 |
0.0 |
1.2 |
| 23 |
1 |
0.0 |
1.2 |
| 27 |
1 |
0.0 |
1.2 |
| 3 |
2 |
0.0 |
2.5 |
| 37 |
1 |
0.0 |
1.2 |
| 45 |
1 |
0.0 |
1.2 |
| 46 |
1 |
0.0 |
1.2 |
| 5 |
1 |
0.0 |
1.2 |
| 50 |
3 |
0.1 |
3.7 |
| 55 |
3 |
0.1 |
3.7 |
| 56 |
1 |
0.0 |
1.2 |
| 58 |
1 |
0.0 |
1.2 |
| 6 |
1 |
0.0 |
1.2 |
| 60 |
1 |
0.0 |
1.2 |
| 61 |
1 |
0.0 |
1.2 |
| 64 |
1 |
0.0 |
1.2 |
| 65 |
1 |
0.0 |
1.2 |
| 7 |
1 |
0.0 |
1.2 |
| 70 |
1 |
0.0 |
1.2 |
| 74 |
1 |
0.0 |
1.2 |
| 75 |
1 |
0.0 |
1.2 |
| 76 |
1 |
0.0 |
1.2 |
| 78 |
1 |
0.0 |
1.2 |
| 8 |
1 |
0.0 |
1.2 |
| 80 |
1 |
0.0 |
1.2 |
| 81 |
1 |
0.0 |
1.2 |
| 82 |
1 |
0.0 |
1.2 |
| 85 |
1 |
0.0 |
1.2 |
| 86 |
1 |
0.0 |
1.2 |
| 88 |
2 |
0.0 |
2.5 |
| 89 |
1 |
0.0 |
1.2 |
| 9 |
3 |
0.1 |
3.7 |
| 90 |
1 |
0.0 |
1.2 |
| 91 |
1 |
0.0 |
1.2 |
| 92 |
1 |
0.0 |
1.2 |
| NA |
5758 |
98.6 |
NA |
| Total |
5839 |
100.0 |
100.0 |
F3: Exercise frequency
- F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
- 4=5-7 times per week
- 3=3-4 times per week
- 2=1-2 times per week
- 1=Less than once per week/do not exercise
f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
levels(f3) <- list(Per_week_5_7="4",
Per_week_3_4="3",
Per_week_1_2="2",
Per_week_less_1="1")
f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
new.d <- data.frame(new.d, f3)
new.d <- apply_labels(new.d, f3 = "exercise")
temp.d <- data.frame (new.d, f3)
result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
| Per_week_5_7 |
959 |
16.4 |
17.6 |
16.4 |
17.6 |
| Per_week_3_4 |
1593 |
27.3 |
29.3 |
43.7 |
46.9 |
| Per_week_1_2 |
1536 |
26.3 |
28.2 |
70.0 |
75.2 |
| Per_week_less_1 |
1351 |
23.1 |
24.8 |
93.1 |
100.0 |
| NA |
400 |
6.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
F4: Minutes of exercise
- F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
- 2=Less than 30 minutes
- 3=30 minutes – 1 hour
- 4=More than 1 hour
- 1=Do not exercise
f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
levels(f4) <- list(Less_than_30_min="2",
Between_30_min_1_hour="3",
More_than_1_hour="4",
Do_not_exercise="1")
f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
new.d <- data.frame(new.d, f4)
new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
temp.d <- data.frame (new.d, f4)
result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F4")
F4
| Less_than_30_min |
999 |
17.1 |
18.4 |
17.1 |
18.4 |
| Between_30_min_1_hour |
2258 |
38.7 |
41.6 |
55.8 |
60.1 |
| More_than_1_hour |
1176 |
20.1 |
21.7 |
75.9 |
81.8 |
| Do_not_exercise |
989 |
16.9 |
18.2 |
92.9 |
100.0 |
| NA |
417 |
7.1 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
F5: Drink alcohol frequency
- F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
- 6=Everyday
- 5=5-6 times per week
- 4=3-4 times per week
- 3=1-2 times per week
- 2=Fewer than once per week
- 1=Did not drink
f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
levels(f5) <- list(Everyday="6",
Per_week_5_6_times="5",
Per_week_3_4_times="4",
Per_week_1_2_times="3",
Per_week_fewer_once="2",
Not_drink="1")
f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
new.d <- data.frame(new.d, f5)
new.d <- apply_labels(new.d, f5 = "how often drink")
temp.d <- data.frame (new.d, f5)
result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f5")
f5
| Everyday |
275 |
4.7 |
4.8 |
4.7 |
4.8 |
| Per_week_5_6_times |
244 |
4.2 |
4.3 |
8.9 |
9.1 |
| Per_week_3_4_times |
628 |
10.8 |
11.0 |
19.6 |
20.1 |
| Per_week_1_2_times |
842 |
14.4 |
14.8 |
34.1 |
34.9 |
| Per_week_fewer_once |
1163 |
19.9 |
20.4 |
54.0 |
55.3 |
| Not_drink |
2547 |
43.6 |
44.7 |
97.6 |
100.0 |
| NA |
140 |
2.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
F6: How many drinks
- F6. When you drank during the past month, how many drinks do you have on a typical occasion?
- 3=3 or more drinks
- 2=1-2 drinks
- 1=Did not drink
f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
levels(f6) <- list(Three_or_more="3",
One_to_two_drinks="2",
Not_drink="1")
f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
new.d <- data.frame(new.d, f6)
new.d <- apply_labels(new.d, f6 = "how many drinks")
temp.d <- data.frame (new.d, f6)
result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f6")
f6
| Three_or_more |
540 |
9.2 |
10.4 |
9.2 |
10.4 |
| One_to_two_drinks |
2473 |
42.4 |
47.8 |
51.6 |
58.3 |
| Not_drink |
2159 |
37.0 |
41.7 |
88.6 |
100.0 |
| NA |
667 |
11.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
F7: Smoking history
- F7. Have you ever smoked at least 100 cigarettes in your lifetime?
- F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
- 555 = “Less than 10”
- 777 = “75+”
- F7a. How many cigarettes do you (or did you) usually smoke per day?
- 1=1-5
- 2=6-10
- 3=11-20
- 4=21-30
- 5=31+
- F7b. Have you quit smoking?
- F7BAge. If yes, At what age did you quit?
- 555 = “Less than 10”
- 777 = “75+”
f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
levels(f7) <- list(Yes="2",
No="1")
f7 <- ordered(f7, c("No","Yes"))
new.d <- data.frame(new.d, f7)
new.d <- apply_labels(new.d, f7 = "smoke")
temp.d <- data.frame (new.d, f7)
result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
| No |
3008 |
51.5 |
54.1 |
51.5 |
54.1 |
| Yes |
2556 |
43.8 |
45.9 |
95.3 |
100.0 |
| NA |
275 |
4.7 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
f7age <- d[,"f7age"]
f7age[which(f7age=="555")]<-"Less_than_10"
f7age[which(f7age=="777")]<-"More_than_75"
new.d <- data.frame(new.d, f7age)
new.d <- apply_labels(new.d, f7age = "age start to smoke")
temp.d <- data.frame (new.d, f7age)
result<-questionr::freq(temp.d$f7age,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
| 0 |
10 |
0.2 |
0.5 |
| 1 |
4 |
0.1 |
0.2 |
| 10 |
14 |
0.2 |
0.7 |
| 11 |
11 |
0.2 |
0.5 |
| 12 |
41 |
0.7 |
2.0 |
| 13 |
60 |
1.0 |
2.9 |
| 14 |
76 |
1.3 |
3.7 |
| 15 |
183 |
3.1 |
8.8 |
| 16 |
229 |
3.9 |
11.0 |
| 17 |
206 |
3.5 |
9.9 |
| 18 |
321 |
5.5 |
15.5 |
| 19 |
168 |
2.9 |
8.1 |
| 2 |
2 |
0.0 |
0.1 |
| 20 |
201 |
3.4 |
9.7 |
| 21 |
116 |
2.0 |
5.6 |
| 22 |
66 |
1.1 |
3.2 |
| 23 |
47 |
0.8 |
2.3 |
| 24 |
29 |
0.5 |
1.4 |
| 25 |
67 |
1.1 |
3.2 |
| 26 |
14 |
0.2 |
0.7 |
| 27 |
17 |
0.3 |
0.8 |
| 28 |
22 |
0.4 |
1.1 |
| 29 |
6 |
0.1 |
0.3 |
| 3 |
3 |
0.1 |
0.1 |
| 30 |
45 |
0.8 |
2.2 |
| 31 |
2 |
0.0 |
0.1 |
| 32 |
8 |
0.1 |
0.4 |
| 33 |
7 |
0.1 |
0.3 |
| 34 |
6 |
0.1 |
0.3 |
| 35 |
21 |
0.4 |
1.0 |
| 36 |
1 |
0.0 |
0.0 |
| 38 |
3 |
0.1 |
0.1 |
| 4 |
1 |
0.0 |
0.0 |
| 40 |
14 |
0.2 |
0.7 |
| 41 |
1 |
0.0 |
0.0 |
| 42 |
1 |
0.0 |
0.0 |
| 44 |
1 |
0.0 |
0.0 |
| 45 |
5 |
0.1 |
0.2 |
| 47 |
3 |
0.1 |
0.1 |
| 48 |
3 |
0.1 |
0.1 |
| 49 |
4 |
0.1 |
0.2 |
| 5 |
1 |
0.0 |
0.0 |
| 50 |
5 |
0.1 |
0.2 |
| 54 |
2 |
0.0 |
0.1 |
| 55 |
2 |
0.0 |
0.1 |
| 56 |
1 |
0.0 |
0.0 |
| 59 |
1 |
0.0 |
0.0 |
| 6 |
1 |
0.0 |
0.0 |
| 60 |
2 |
0.0 |
0.1 |
| 62 |
1 |
0.0 |
0.0 |
| 65 |
1 |
0.0 |
0.0 |
| 69 |
2 |
0.0 |
0.1 |
| 7 |
4 |
0.1 |
0.2 |
| 70 |
1 |
0.0 |
0.0 |
| 8 |
4 |
0.1 |
0.2 |
| 9 |
7 |
0.1 |
0.3 |
| Less_than_10 |
2 |
0.0 |
0.1 |
| NA |
3763 |
64.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
f7a <- as.factor(d[,"f7a"])
# Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
levels(f7a) <- list(One_to_five="1",
Six_to_ten="2",
Eleven_to_twenty="3",
Twentyone_to_Thirty="4",
Older_31="5")
f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))
new.d <- data.frame(new.d, f7a)
new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
temp.d <- data.frame (new.d, f7a)
result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
| One_to_five |
917 |
15.7 |
35.4 |
15.7 |
35.4 |
| Six_to_ten |
774 |
13.3 |
29.9 |
29.0 |
65.2 |
| Eleven_to_twenty |
664 |
11.4 |
25.6 |
40.3 |
90.9 |
| Twentyone_to_Thirty |
168 |
2.9 |
6.5 |
43.2 |
97.3 |
| Older_31 |
69 |
1.2 |
2.7 |
44.4 |
100.0 |
| NA |
3247 |
55.6 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
f7b <- as.factor(d[,"f7b"])
# Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
levels(f7b) <- list(No="1",
Yes="2")
new.d <- data.frame(new.d, f7b)
new.d <- apply_labels(new.d, f7b = "quit smoking")
temp.d <- data.frame (new.d, f7b)
result<-questionr::freq(temp.d$f7b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
| No |
572 |
9.8 |
21.7 |
| Yes |
2068 |
35.4 |
78.3 |
| NA |
3199 |
54.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
f7bage <- d[,"f7bage"]
f7bage[which(f7bage=="555")]<-"Less_than_10"
f7bage[which(f7bage=="777")]<-"More_than_75"
new.d <- data.frame(new.d, f7bage)
new.d <- apply_labels(new.d, f7bage = "age quit smoking")
temp.d <- data.frame (new.d, f7bage)
result<-questionr::freq(temp.d$f7bage,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
| 1 |
2 |
0.0 |
0.1 |
| 11 |
2 |
0.0 |
0.1 |
| 12 |
1 |
0.0 |
0.1 |
| 14 |
1 |
0.0 |
0.1 |
| 15 |
2 |
0.0 |
0.1 |
| 16 |
6 |
0.1 |
0.3 |
| 17 |
8 |
0.1 |
0.4 |
| 18 |
8 |
0.1 |
0.4 |
| 19 |
13 |
0.2 |
0.7 |
| 20 |
36 |
0.6 |
1.8 |
| 21 |
32 |
0.5 |
1.6 |
| 22 |
26 |
0.4 |
1.3 |
| 23 |
38 |
0.7 |
1.9 |
| 24 |
27 |
0.5 |
1.4 |
| 25 |
61 |
1.0 |
3.1 |
| 26 |
21 |
0.4 |
1.1 |
| 27 |
27 |
0.5 |
1.4 |
| 28 |
50 |
0.9 |
2.5 |
| 29 |
25 |
0.4 |
1.3 |
| 30 |
106 |
1.8 |
5.3 |
| 31 |
33 |
0.6 |
1.7 |
| 32 |
30 |
0.5 |
1.5 |
| 33 |
27 |
0.5 |
1.4 |
| 34 |
30 |
0.5 |
1.5 |
| 35 |
110 |
1.9 |
5.5 |
| 36 |
24 |
0.4 |
1.2 |
| 37 |
18 |
0.3 |
0.9 |
| 38 |
31 |
0.5 |
1.6 |
| 39 |
23 |
0.4 |
1.2 |
| 4 |
2 |
0.0 |
0.1 |
| 40 |
133 |
2.3 |
6.7 |
| 41 |
26 |
0.4 |
1.3 |
| 42 |
37 |
0.6 |
1.9 |
| 43 |
25 |
0.4 |
1.3 |
| 44 |
19 |
0.3 |
1.0 |
| 45 |
78 |
1.3 |
3.9 |
| 46 |
20 |
0.3 |
1.0 |
| 47 |
26 |
0.4 |
1.3 |
| 48 |
32 |
0.5 |
1.6 |
| 49 |
27 |
0.5 |
1.4 |
| 50 |
132 |
2.3 |
6.6 |
| 51 |
25 |
0.4 |
1.3 |
| 52 |
37 |
0.6 |
1.9 |
| 53 |
22 |
0.4 |
1.1 |
| 54 |
23 |
0.4 |
1.2 |
| 55 |
63 |
1.1 |
3.2 |
| 56 |
33 |
0.6 |
1.7 |
| 57 |
23 |
0.4 |
1.2 |
| 58 |
38 |
0.7 |
1.9 |
| 59 |
32 |
0.5 |
1.6 |
| 6 |
1 |
0.0 |
0.1 |
| 60 |
61 |
1.0 |
3.1 |
| 61 |
20 |
0.3 |
1.0 |
| 62 |
33 |
0.6 |
1.7 |
| 63 |
22 |
0.4 |
1.1 |
| 64 |
18 |
0.3 |
0.9 |
| 65 |
45 |
0.8 |
2.3 |
| 66 |
19 |
0.3 |
1.0 |
| 67 |
17 |
0.3 |
0.9 |
| 68 |
18 |
0.3 |
0.9 |
| 69 |
16 |
0.3 |
0.8 |
| 7 |
1 |
0.0 |
0.1 |
| 70 |
19 |
0.3 |
1.0 |
| 71 |
8 |
0.1 |
0.4 |
| 72 |
4 |
0.1 |
0.2 |
| 73 |
5 |
0.1 |
0.3 |
| 74 |
4 |
0.1 |
0.2 |
| 75 |
3 |
0.1 |
0.2 |
| 76 |
2 |
0.0 |
0.1 |
| 78 |
3 |
0.1 |
0.2 |
| 79 |
1 |
0.0 |
0.1 |
| 8 |
2 |
0.0 |
0.1 |
| 80 |
1 |
0.0 |
0.1 |
| 86 |
1 |
0.0 |
0.1 |
| 9 |
1 |
0.0 |
0.1 |
| NA |
3843 |
65.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G1: Marital status
- G1. What is your current marital status?
- 1=Married, or living with a partner
- 2=Separated
- 3=Divorced
- 4=Widowed
- 5=Never Married
g1 <- as.factor(d[,"g1"])
# Make "*" to NA
g1[which(g1=="*")]<-"NA"
levels(g1) <- list(Married_partner="1",
Separated="2",
Divorced="3",
Widowed="4",
Never_Married="5")
g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
new.d <- data.frame(new.d, g1)
new.d <- apply_labels(new.d, g1 = "marital status")
temp.d <- data.frame (new.d, g1)
result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g1")
g1
| Married_partner |
3682 |
63.1 |
64.5 |
63.1 |
64.5 |
| Separated |
204 |
3.5 |
3.6 |
66.6 |
68.0 |
| Divorced |
928 |
15.9 |
16.2 |
82.4 |
84.3 |
| Widowed |
353 |
6.0 |
6.2 |
88.5 |
90.5 |
| Never_Married |
545 |
9.3 |
9.5 |
97.8 |
100.0 |
| NA |
127 |
2.2 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
G2: With whom do you live
- G2. With whom do you live? Mark all that apply.
- G2_1: 1=Live alone
- G2_2: 1=A spouse or partner
- G2_3: 1=Other family
- G2_4: 1=Other people (non-family)
- G2_5: 1=Pets
g2_1 <- as.factor(d[,"g2_1"])
levels(g2_1) <- list(Live_alone="1")
new.d <- data.frame(new.d, g2_1)
new.d <- apply_labels(new.d, g2_1 = "Live alone")
temp.d <- data.frame (new.d, g2_1)
result<-questionr::freq(temp.d$g2_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
| Live_alone |
1273 |
21.8 |
100 |
| NA |
4566 |
78.2 |
NA |
| Total |
5839 |
100.0 |
100 |
g2_2 <- as.factor(d[,"g2_2"])
levels(g2_2) <- list(spouse_partner="1")
new.d <- data.frame(new.d, g2_2)
new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
temp.d <- data.frame (new.d, g2_2)
result<-questionr::freq(temp.d$g2_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
| spouse_partner |
3798 |
65 |
100 |
| NA |
2041 |
35 |
NA |
| Total |
5839 |
100 |
100 |
g2_3 <- as.factor(d[,"g2_3"])
levels(g2_3) <- list(Other_family="1")
new.d <- data.frame(new.d, g2_3)
new.d <- apply_labels(new.d, g2_3 = "Other family")
temp.d <- data.frame (new.d, g2_3)
result<-questionr::freq(temp.d$g2_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
| Other_family |
819 |
14 |
100 |
| NA |
5020 |
86 |
NA |
| Total |
5839 |
100 |
100 |
g2_4 <- as.factor(d[,"g2_4"])
levels(g2_4) <- list(Other_non_family="1")
new.d <- data.frame(new.d, g2_4)
new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
temp.d <- data.frame (new.d, g2_4)
result<-questionr::freq(temp.d$g2_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
| Other_non_family |
134 |
2.3 |
100 |
| NA |
5705 |
97.7 |
NA |
| Total |
5839 |
100.0 |
100 |
g2_5 <- as.factor(d[,"g2_5"])
levels(g2_5) <- list(Pets="1")
new.d <- data.frame(new.d, g2_5)
new.d <- apply_labels(new.d, g2_5 = "Pets")
temp.d <- data.frame (new.d, g2_5)
result<-questionr::freq(temp.d$g2_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
| Pets |
345 |
5.9 |
100 |
| NA |
5494 |
94.1 |
NA |
| Total |
5839 |
100.0 |
100 |
G3: Identify yourself
- G3. How do you identify yourself?
- 1=Straight/heterosexual
- 2=Bisexual
- 3=Gay/homosexual/same gender loving
- 4=Other
- 99=Prefer not to answer
g3 <- as.factor(d[,"g3"])
# Make "*" to NA
g3[which(g3=="*")]<-"NA"
levels(g3) <- list(heterosexual="1",
Bisexual="2",
homosexual="3",
Other="4",
Prefer_not_to_answer="99")
g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))
new.d <- data.frame(new.d, g3)
new.d <- apply_labels(new.d, g3 = "identify yourself")
temp.d <- data.frame (new.d, g3)
result<-questionr::freq(temp.d$g3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g3")
g3
| heterosexual |
5366 |
91.9 |
95.1 |
| Bisexual |
38 |
0.7 |
0.7 |
| homosexual |
102 |
1.7 |
1.8 |
| Other |
30 |
0.5 |
0.5 |
| Prefer_not_to_answer |
108 |
1.8 |
1.9 |
| NA |
195 |
3.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G3 Other: Identify yourself
g3other <- d[,"g3other"]
new.d <- data.frame(new.d, g3other)
new.d <- apply_labels(new.d, g3other = "g3other")
temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G3 Other")
G3 Other
| —- |
1 |
0.0 |
1.4 |
| 100% man |
1 |
0.0 |
1.4 |
| a man |
1 |
0.0 |
1.4 |
| A man |
3 |
0.1 |
4.2 |
| A person straight. |
1 |
0.0 |
1.4 |
| All man |
1 |
0.0 |
1.4 |
| All man by the grace of God |
1 |
0.0 |
1.4 |
| Allosexual |
1 |
0.0 |
1.4 |
| Black-A |
1 |
0.0 |
1.4 |
| But not sexually active |
1 |
0.0 |
1.4 |
| Christian. |
1 |
0.0 |
1.4 |
| Do not want to be confused like women nothing else |
1 |
0.0 |
1.4 |
| Get hard, I wish it would. |
1 |
0.0 |
1.4 |
| Have a man! He took something from me that I can’t get back. |
1 |
0.0 |
1.4 |
| Human |
2 |
0.0 |
2.8 |
| Human! |
1 |
0.0 |
1.4 |
| I am man married to woman |
1 |
0.0 |
1.4 |
| I am straight |
1 |
0.0 |
1.4 |
| I am straight. |
2 |
0.0 |
2.8 |
| I consider myself a white girl |
1 |
0.0 |
1.4 |
| I enjoy women only. |
1 |
0.0 |
1.4 |
| Just straight |
1 |
0.0 |
1.4 |
| Love women only |
1 |
0.0 |
1.4 |
| Male |
3 |
0.1 |
4.2 |
| Male/Married |
1 |
0.0 |
1.4 |
| Man |
3 |
0.1 |
4.2 |
| Man (male) |
1 |
0.0 |
1.4 |
| Man straight |
1 |
0.0 |
1.4 |
| Man’s man |
1 |
0.0 |
1.4 |
| Man/Human being |
1 |
0.0 |
1.4 |
| Married |
1 |
0.0 |
1.4 |
| No |
1 |
0.0 |
1.4 |
| No sex |
1 |
0.0 |
1.4 |
| None heterosexual |
1 |
0.0 |
1.4 |
| Normal human being |
1 |
0.0 |
1.4 |
| Normal man |
1 |
0.0 |
1.4 |
| Not gay thank God. |
1 |
0.0 |
1.4 |
| Self |
1 |
0.0 |
1.4 |
| Start |
1 |
0.0 |
1.4 |
| Straight |
9 |
0.2 |
12.7 |
| Straight earl. |
1 |
0.0 |
1.4 |
| Straight I like women |
1 |
0.0 |
1.4 |
| Straight male |
2 |
0.0 |
2.8 |
| Straight male. |
1 |
0.0 |
1.4 |
| Straight man |
1 |
0.0 |
1.4 |
| Straight man all |
1 |
0.0 |
1.4 |
| Straight person |
1 |
0.0 |
1.4 |
| Straight. |
3 |
0.1 |
4.2 |
| Strong black man |
1 |
0.0 |
1.4 |
| What God made me a man |
1 |
0.0 |
1.4 |
| Whole man. |
1 |
0.0 |
1.4 |
| With a woman |
1 |
0.0 |
1.4 |
| NA |
5768 |
98.8 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G4: Education
- G4. What is the HIGHEST level of education you, your father, and your mother have completed?
- 1=Grade school or less
- 2=Some high school
- 3=High school graduate or GED
- 4=Vocational school
- 5=Some college
- 6=Associate’s degree
- 7=College graduate (Bachelor’s degree)
- 8=Some graduate education
- 9=Graduate degree
- 88=Don’t know
g4a <- as.factor(d[,"g4a"])
# Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
levels(g4a) <- list(Grade_school_or_less="1",
Some_high_school="2",
High_school_graduate_GED="3",
Vocational_school="4",
Some_college="5",
Associate_degree="6",
College_graduate="7",
Some_graduate_education="8",
Graduate_degree="9")
g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))
new.d <- data.frame(new.d, g4a)
new.d <- apply_labels(new.d, g4a = "education")
temp.d <- data.frame (new.d, g4a)
result<-questionr::freq(temp.d$g4a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
| Grade_school_or_less |
139 |
2.4 |
2.6 |
| Some_high_school |
372 |
6.4 |
7.0 |
| High_school_graduate_GED |
1170 |
20.0 |
22.0 |
| Vocational_school |
188 |
3.2 |
3.5 |
| Some_college |
1221 |
20.9 |
23.0 |
| Associate_degree |
482 |
8.3 |
9.1 |
| College_graduate |
777 |
13.3 |
14.6 |
| Some_graduate_education |
194 |
3.3 |
3.6 |
| Graduate_degree |
774 |
13.3 |
14.6 |
| NA |
522 |
8.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
g4b <- as.factor(d[,"g4b"])
# Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
levels(g4b) <- list(Grade_school_or_less="1",
Some_high_school="2",
High_school_graduate_GED="3",
Vocational_school="4",
Some_college="5",
Associate_degree="6",
College_graduate="7",
Some_graduate_education="8",
Graduate_degree="9",
Dont_know="88")
g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))
new.d <- data.frame(new.d, g4b)
new.d <- apply_labels(new.d, g4b = "education-father")
temp.d <- data.frame (new.d, g4b)
result<-questionr::freq(temp.d$g4b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
| Grade_school_or_less |
1324 |
22.7 |
25.3 |
| Some_high_school |
740 |
12.7 |
14.1 |
| High_school_graduate_GED |
1191 |
20.4 |
22.7 |
| Vocational_school |
162 |
2.8 |
3.1 |
| Some_college |
215 |
3.7 |
4.1 |
| Associate_degree |
89 |
1.5 |
1.7 |
| College_graduate |
190 |
3.3 |
3.6 |
| Some_graduate_education |
31 |
0.5 |
0.6 |
| Graduate_degree |
149 |
2.6 |
2.8 |
| Dont_know |
1145 |
19.6 |
21.9 |
| NA |
603 |
10.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
g4c <- as.factor(d[,"g4c"])
# Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
levels(g4c) <- list(Grade_school_or_less="1",
Some_high_school="2",
High_school_graduate_GED="3",
Vocational_school="4",
Some_college="5",
Associate_degree="6",
College_graduate="7",
Some_graduate_education="8",
Graduate_degree="9",
Dont_know="88")
g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))
new.d <- data.frame(new.d, g4c)
new.d <- apply_labels(new.d, g4c = "education-mother")
temp.d <- data.frame (new.d, g4c)
result<-questionr::freq(temp.d$g4c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
| Grade_school_or_less |
965 |
16.5 |
18.4 |
| Some_high_school |
824 |
14.1 |
15.7 |
| High_school_graduate_GED |
1522 |
26.1 |
29.0 |
| Vocational_school |
210 |
3.6 |
4.0 |
| Some_college |
306 |
5.2 |
5.8 |
| Associate_degree |
172 |
2.9 |
3.3 |
| College_graduate |
257 |
4.4 |
4.9 |
| Some_graduate_education |
38 |
0.7 |
0.7 |
| Graduate_degree |
185 |
3.2 |
3.5 |
| Dont_know |
761 |
13.0 |
14.5 |
| NA |
599 |
10.3 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G5: Job
- G5. Which one of the following best describes what you currently do?
- 1=Currently working full-time
- 2=Currently working part-time
- 3=Looking for work, unemployed
- 4=Retired
- 5=On disability permanently
- 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
- 7=Volunteer work/work without pay
- 8=Other
g5 <- as.factor(d[,"g5"])
# Make "*" to NA
g5[which(g5=="*")]<-"NA"
levels(g5) <- list(full_time="1",
part_time="2",
unemployed="3",
Retired="4",
disability_permanently="5",
disability_for_a_time="6",
Volunteer_work="7",
Other="8")
g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))
new.d <- data.frame(new.d, g5)
new.d <- apply_labels(new.d, g5 = "job")
temp.d <- data.frame (new.d, g5)
result<-questionr::freq(temp.d$g5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g5")
g5
| full_time |
1514 |
25.9 |
27.4 |
| part_time |
313 |
5.4 |
5.7 |
| unemployed |
96 |
1.6 |
1.7 |
| Retired |
2688 |
46.0 |
48.7 |
| disability_permanently |
685 |
11.7 |
12.4 |
| disability_for_a_time |
59 |
1.0 |
1.1 |
| Volunteer_work |
32 |
0.5 |
0.6 |
| Other |
130 |
2.2 |
2.4 |
| NA |
322 |
5.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G5 Other: job
g5other <- d[,"g5other"]
new.d <- data.frame(new.d, g5other)
new.d <- apply_labels(new.d, g5other = "g5other")
temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G5 Other")
G5 Other
| 100% physically and mentally disabled. |
1 |
0.0 |
0.4 |
| 60% disable veteran |
1 |
0.0 |
0.4 |
| Also perform volunteer work each week |
1 |
0.0 |
0.4 |
| And publish a newspaper. |
1 |
0.0 |
0.4 |
| Artist |
1 |
0.0 |
0.4 |
| back and forth to doctor but need work (looking for work, unemployed) |
1 |
0.0 |
0.4 |
| business owner |
1 |
0.0 |
0.4 |
| Business Owner |
1 |
0.0 |
0.4 |
| Can’t make but $85 over my monthly allotment. |
1 |
0.0 |
0.4 |
| Church. |
1 |
0.0 |
0.4 |
| Coach |
1 |
0.0 |
0.4 |
| Combat wounded in Vietnam |
1 |
0.0 |
0.4 |
| Commissioner at voting pole |
1 |
0.0 |
0.4 |
| Computer consultant. |
1 |
0.0 |
0.4 |
| Contract work |
1 |
0.0 |
0.4 |
| Corona virus shut down |
1 |
0.0 |
0.4 |
| Covid 19 |
1 |
0.0 |
0.4 |
| Covid-19 Laid Off |
1 |
0.0 |
0.4 |
| Covid-19. Work part-time for SFUSD schools. |
1 |
0.0 |
0.4 |
| Currently laid off, because of Covid 19. |
1 |
0.0 |
0.4 |
| Currently on Furlough due to the CoVid-19 Pandemic |
1 |
0.0 |
0.4 |
| Cut grass and pick up cans. |
1 |
0.0 |
0.4 |
| DDS, Dentist practicing 2 days a week |
1 |
0.0 |
0.4 |
| Digital Art work |
1 |
0.0 |
0.4 |
| Disability retirement |
1 |
0.0 |
0.4 |
| Disabled American veteran. |
1 |
0.0 |
0.4 |
| Disabled from birth, intellectually disabled. |
1 |
0.0 |
0.4 |
| Disabled from my deadly double 18 wheeler truck. The truck driver died. |
1 |
0.0 |
0.4 |
| disabled self-employed |
1 |
0.0 |
0.4 |
| Disabled vet/retired from DHS |
1 |
0.0 |
0.4 |
| Do help my daughter with cleaning offices. |
1 |
0.0 |
0.4 |
| Due to pandemic, working continuously for 45-plus years. Worked two jobs for 15 years. |
1 |
0.0 |
0.4 |
| Elected official-city council |
1 |
0.0 |
0.4 |
| Entrepreneur |
1 |
0.0 |
0.4 |
| Force to Social Security |
1 |
0.0 |
0.4 |
| Full-time farmer. |
1 |
0.0 |
0.4 |
| full-time graduate student and work part-time |
1 |
0.0 |
0.4 |
| Furloughed COVID 19 Pandemic |
1 |
0.0 |
0.4 |
| Had surgery 7-30-20, unable to work at this time. |
1 |
0.0 |
0.4 |
| Health care/hospital shipping and receiving |
1 |
0.0 |
0.4 |
| Hurt On The Job |
1 |
0.0 |
0.4 |
| I also work as Uber driver part time |
1 |
0.0 |
0.4 |
| I am a very old 95 year old man. |
1 |
0.0 |
0.4 |
| I am living off Gov. income —-. |
1 |
0.0 |
0.4 |
| I am self employed |
1 |
0.0 |
0.4 |
| I have had at least 6 heart attacks and currently have 9 stints in my heart and have been diagnosed with heart failure. Waiting on disability. |
1 |
0.0 |
0.4 |
| I have had at least 6 heart attacks, 9 stints —- have heart —- waiting on disability. |
1 |
0.0 |
0.4 |
| I still work sometimes. |
1 |
0.0 |
0.4 |
| I was working fulltime as a retail manager up until covid-19. Last worked March 2020. |
1 |
0.0 |
0.4 |
| I work in the information/technology sector, due to the importation of foreign workers, I have on worked 4.5 years since 2005, racism has been a large reason for my unemployment |
1 |
0.0 |
0.4 |
| I work part time and I am self employed |
1 |
0.0 |
0.4 |
| I’m a lawyer for 44 years. |
1 |
0.0 |
0.4 |
| I’m on SSDI trying to go back to work. |
1 |
0.0 |
0.4 |
| Import/retail sale of handmade crafts after retirement. |
1 |
0.0 |
0.4 |
| In business. |
1 |
0.0 |
0.4 |
| Insurance Agent: slow due to covid |
1 |
0.0 |
0.4 |
| Justice of the peace St. John Parish |
1 |
0.0 |
0.4 |
| Lack of work because of Covid 19 |
1 |
0.0 |
0.4 |
| Lawn and lawn mower repair occasionally |
1 |
0.0 |
0.4 |
| Looking to work soon |
1 |
0.0 |
0.4 |
| Lung cancer stage 4-hospice care. |
1 |
0.0 |
0.4 |
| Manage income property |
1 |
0.0 |
0.4 |
| Medically retired due to surgery got to have more surgery. |
1 |
0.0 |
0.4 |
| Mentoring 1st year principals |
1 |
0.0 |
0.4 |
| Minister |
1 |
0.0 |
0.4 |
| Minister, author, poet |
1 |
0.0 |
0.4 |
| Mow lawns, remodel houses. |
1 |
0.0 |
0.4 |
| Musician |
2 |
0.0 |
0.8 |
| My family help with my bills and rent |
1 |
0.0 |
0.4 |
| Need to be on disability permanently |
1 |
0.0 |
0.4 |
| No work due to covid/district for which I sub, will hopefully return to normal in September. |
1 |
0.0 |
0.4 |
| Not able to work |
1 |
0.0 |
0.4 |
| Not able to work I have a bad back, I’m trying to get disability. |
1 |
0.0 |
0.4 |
| Not working due to Covid 19 pandemic |
1 |
0.0 |
0.4 |
| Not working, don’t feel good all the time. |
1 |
0.0 |
0.4 |
| Nothing at the moment |
1 |
0.0 |
0.4 |
| Now receiving Social Security benefits |
1 |
0.0 |
0.4 |
| Occasional Uber driver |
1 |
0.0 |
0.4 |
| Off work due to medical treatments ongoing. |
1 |
0.0 |
0.4 |
| On disability due to heart procedure. |
1 |
0.0 |
0.4 |
| On disability, for another year, had kidney transplant |
1 |
0.0 |
0.4 |
| On disability. |
1 |
0.0 |
0.4 |
| on social security |
1 |
0.0 |
0.4 |
| On temporary disability (workers compensation) |
1 |
0.0 |
0.4 |
| Out on sick leave, after Liphoma removal. |
1 |
0.0 |
0.4 |
| Own a business |
1 |
0.0 |
0.4 |
| Own my own business. |
1 |
0.0 |
0.4 |
| Own PC home |
1 |
0.0 |
0.4 |
| Own vending business |
1 |
0.0 |
0.4 |
| Pandemic furlough since 3-2020. |
1 |
0.0 |
0.4 |
| Part time business owner of ACN. |
1 |
0.0 |
0.4 |
| Part time peel around |
1 |
0.0 |
0.4 |
| Part time self employed |
2 |
0.0 |
0.8 |
| Part time, business owner (pandemic) |
1 |
0.0 |
0.4 |
| part-time/retired |
1 |
0.0 |
0.4 |
| Pastor |
1 |
0.0 |
0.4 |
| Pastor at church |
1 |
0.0 |
0.4 |
| Pastor. |
1 |
0.0 |
0.4 |
| PhD program and part time work |
1 |
0.0 |
0.4 |
| Photographer work from home table top photos |
1 |
0.0 |
0.4 |
| Plus photographer |
1 |
0.0 |
0.4 |
| Prefer not to answer. |
1 |
0.0 |
0.4 |
| Private investor |
1 |
0.0 |
0.4 |
| recently laid off, now deciding between retirement and job search |
1 |
0.0 |
0.4 |
| Regular temp |
1 |
0.0 |
0.4 |
| Retired |
14 |
0.2 |
5.8 |
| Retired (2005) |
1 |
0.0 |
0.4 |
| retired (human resources) but off and on part-time and full time self (EdD level education projects and consulting))) |
1 |
0.0 |
0.4 |
| Retired 23 years from city of Los Angeles |
1 |
0.0 |
0.4 |
| Retired after 30 years in mental health. Working part time in D.D. |
1 |
0.0 |
0.4 |
| Retired and disability permanently |
1 |
0.0 |
0.4 |
| Retired and full-time doctoral student |
1 |
0.0 |
0.4 |
| retired and on disability permanently |
1 |
0.0 |
0.4 |
| Retired and on disability permanently |
1 |
0.0 |
0.4 |
| Retired and part time work |
1 |
0.0 |
0.4 |
| Retired and Self Employed |
1 |
0.0 |
0.4 |
| Retired and worked as consultant |
1 |
0.0 |
0.4 |
| Retired and working |
1 |
0.0 |
0.4 |
| Retired but assisting partner at her business daily |
1 |
0.0 |
0.4 |
| Retired but do volunteer work, sit on board. |
1 |
0.0 |
0.4 |
| Retired but does a lot of work for the church and is a photographer |
1 |
0.0 |
0.4 |
| Retired but doing some work (acting) |
1 |
0.0 |
0.4 |
| retired but work a little to stay active |
1 |
0.0 |
0.4 |
| Retired but work part time, 2 days a week. |
1 |
0.0 |
0.4 |
| Retired but would love to work. |
1 |
0.0 |
0.4 |
| retired by still works fulltime |
1 |
0.0 |
0.4 |
| Retired consultant ESL teacher 4 days 7 hour a day |
1 |
0.0 |
0.4 |
| Retired fighting for permanent from VA. |
1 |
0.0 |
0.4 |
| Retired I do in home care PT |
1 |
0.0 |
0.4 |
| Retired military |
2 |
0.0 |
0.8 |
| Retired military and public education and now pastor of a CME church. |
1 |
0.0 |
0.4 |
| Retired military working full time security |
1 |
0.0 |
0.4 |
| Retired military. |
1 |
0.0 |
0.4 |
| retired part time work |
1 |
0.0 |
0.4 |
| Retired part-time work —-. |
1 |
0.0 |
0.4 |
| Retired professional firefighter-Fire Captain |
1 |
0.0 |
0.4 |
| Retired sitter for my mother |
1 |
0.0 |
0.4 |
| Retired with Pension and have small construction business |
1 |
0.0 |
0.4 |
| Retired-part time insurance sales |
1 |
0.0 |
0.4 |
| retired, but doing consulting work |
1 |
0.0 |
0.4 |
| Retired, but looking for work. |
1 |
0.0 |
0.4 |
| Retired, but serve on multiple boards and do a great deal of community work, including using the arts (e.g., theatre, television, film) to promote health equity and social justice. |
1 |
0.0 |
0.4 |
| Retired, but work on our farm, some construction |
1 |
0.0 |
0.4 |
| Retired, plus volunteer work |
1 |
0.0 |
0.4 |
| Retired, volunteer (W/P) |
1 |
0.0 |
0.4 |
| Retired, work on occassions |
1 |
0.0 |
0.4 |
| Retired, working part time |
1 |
0.0 |
0.4 |
| retired/disabled |
1 |
0.0 |
0.4 |
| Retiring 6-30-2021, Laco Fire Dept.. |
1 |
0.0 |
0.4 |
| Sales consultant |
1 |
0.0 |
0.4 |
| school crossing guard |
1 |
0.0 |
0.4 |
| Seeking disability. |
1 |
0.0 |
0.4 |
| Self |
2 |
0.0 |
0.8 |
| self employed |
3 |
0.1 |
1.2 |
| Self employed |
15 |
0.3 |
6.2 |
| Self Employed |
1 |
0.0 |
0.4 |
| SELF EMPLOYED |
1 |
0.0 |
0.4 |
| Self employed auto mechanic |
1 |
0.0 |
0.4 |
| Self employed in property management and non profit work |
1 |
0.0 |
0.4 |
| Self employed martial arts instructor —- |
1 |
0.0 |
0.4 |
| Self employed. |
1 |
0.0 |
0.4 |
| self-employed |
1 |
0.0 |
0.4 |
| Self-employed (caterer). |
1 |
0.0 |
0.4 |
| self-employed tax preparer |
1 |
0.0 |
0.4 |
| Self-employed. |
1 |
0.0 |
0.4 |
| Semi retired (programmer) |
1 |
0.0 |
0.4 |
| Semi retired, work 3 days a week, 27 hours a week |
1 |
0.0 |
0.4 |
| semi-retired |
2 |
0.0 |
0.8 |
| Several jobs in the side such as: mowing lawns, cleaning buildings. |
1 |
0.0 |
0.4 |
| Ship captain |
1 |
0.0 |
0.4 |
| Sick leave. |
1 |
0.0 |
0.4 |
| Simi Retired- self employed |
1 |
0.0 |
0.4 |
| Since April of 2020, I have not had my own residence and have been living with family or friends. |
1 |
0.0 |
0.4 |
| Small business owner |
1 |
0.0 |
0.4 |
| Social security |
1 |
0.0 |
0.4 |
| Social Security |
2 |
0.0 |
0.8 |
| Social Security income SSI. |
1 |
0.0 |
0.4 |
| Sold business 11/2018. Semi-Retired. |
1 |
0.0 |
0.4 |
| SS |
1 |
0.0 |
0.4 |
| SSI |
3 |
0.1 |
1.2 |
| Started Rail Consultant company |
1 |
0.0 |
0.4 |
| Starting new home-healthcare and insurance brokerage business |
1 |
0.0 |
0.4 |
| Stay home now, because of Corona Virus. |
1 |
0.0 |
0.4 |
| Sub/teacher/Magnet School/Townview |
1 |
0.0 |
0.4 |
| Substitute teacher/graphic designer website designer |
1 |
0.0 |
0.4 |
| Take care of a 98 year old man. |
1 |
0.0 |
0.4 |
| Teach tennis free to kids |
1 |
0.0 |
0.4 |
| Technical contract worker |
1 |
0.0 |
0.4 |
| Temp job |
1 |
0.0 |
0.4 |
| Trying to get disability |
1 |
0.0 |
0.4 |
| Unemployed |
2 |
0.0 |
0.8 |
| Unemployment over nine years. |
1 |
0.0 |
0.4 |
| Varies |
1 |
0.0 |
0.4 |
| Vet disability Vietnam |
1 |
0.0 |
0.4 |
| Vocational |
1 |
0.0 |
0.4 |
| Volunteer at church |
1 |
0.0 |
0.4 |
| Work full with disability aka left |
1 |
0.0 |
0.4 |
| Work to supplement Retirement Income |
1 |
0.0 |
0.4 |
| Workers compensation |
1 |
0.0 |
0.4 |
| Working full time as a retired annuitant. |
1 |
0.0 |
0.4 |
| Working occasionally. |
1 |
0.0 |
0.4 |
| Write |
1 |
0.0 |
0.4 |
| yard work |
1 |
0.0 |
0.4 |
| Yard work. |
1 |
0.0 |
0.4 |
| NA |
5597 |
95.9 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G6: Health insurance
- G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
- G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
- G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
- G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
- G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
- G6_5: 1= Medicaid or other state provided insurance
- G6_6: 1=Medicare/government insurance
- G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
- G6_8: 1=I do not have any medical insurance
g6_1 <- as.factor(d[,"g6_1"])
levels(g6_1) <- list(Insurance_employer="1")
new.d <- data.frame(new.d, g6_1)
new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
temp.d <- data.frame (new.d, g6_1)
result<-questionr::freq(temp.d$g6_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
| Insurance_employer |
2084 |
35.7 |
100 |
| NA |
3755 |
64.3 |
NA |
| Total |
5839 |
100.0 |
100 |
g6_2 <- as.factor(d[,"g6_2"])
levels(g6_2) <- list(Insurance_family="1")
new.d <- data.frame(new.d, g6_2)
new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
temp.d <- data.frame (new.d, g6_2)
result<-questionr::freq(temp.d$g6_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
| Insurance_family |
675 |
11.6 |
100 |
| NA |
5164 |
88.4 |
NA |
| Total |
5839 |
100.0 |
100 |
g6_3 <- as.factor(d[,"g6_3"])
levels(g6_3) <- list(Insurance_insurance_company="1")
new.d <- data.frame(new.d, g6_3)
new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
temp.d <- data.frame (new.d, g6_3)
result<-questionr::freq(temp.d$g6_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
| Insurance_insurance_company |
386 |
6.6 |
100 |
| NA |
5453 |
93.4 |
NA |
| Total |
5839 |
100.0 |
100 |
g6_4 <- as.factor(d[,"g6_4"])
levels(g6_4) <- list(Insurance_exchange="1")
new.d <- data.frame(new.d, g6_4)
new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
temp.d <- data.frame (new.d, g6_4)
result<-questionr::freq(temp.d$g6_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
| Insurance_exchange |
161 |
2.8 |
100 |
| NA |
5678 |
97.2 |
NA |
| Total |
5839 |
100.0 |
100 |
g6_5 <- as.factor(d[,"g6_5"])
levels(g6_5) <- list(Medicaid_state="1")
new.d <- data.frame(new.d, g6_5)
new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
temp.d <- data.frame (new.d, g6_5)
result<-questionr::freq(temp.d$g6_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
| Medicaid_state |
788 |
13.5 |
100 |
| NA |
5051 |
86.5 |
NA |
| Total |
5839 |
100.0 |
100 |
g6_6 <- as.factor(d[,"g6_6"])
levels(g6_6) <- list(Medicare_government="1")
new.d <- data.frame(new.d, g6_6)
new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
temp.d <- data.frame (new.d, g6_6)
result<-questionr::freq(temp.d$g6_6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
| Medicare_government |
2688 |
46 |
100 |
| NA |
3151 |
54 |
NA |
| Total |
5839 |
100 |
100 |
g6_7 <- as.factor(d[,"g6_7"])
levels(g6_7) <- list(VA_Military="1")
new.d <- data.frame(new.d, g6_7)
new.d <- apply_labels(new.d, g6_7 = "VA_Military")
temp.d <- data.frame (new.d, g6_7)
result<-questionr::freq(temp.d$g6_7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
| VA_Military |
919 |
15.7 |
100 |
| NA |
4920 |
84.3 |
NA |
| Total |
5839 |
100.0 |
100 |
g6_8 <- as.factor(d[,"g6_8"])
levels(g6_8) <- list(Do_not_have="1")
new.d <- data.frame(new.d, g6_8)
new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
temp.d <- data.frame (new.d, g6_8)
result<-questionr::freq(temp.d$g6_8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
| Do_not_have |
96 |
1.6 |
100 |
| NA |
5743 |
98.4 |
NA |
| Total |
5839 |
100.0 |
100 |
G7: Income
- G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
- 1=Less than $15,000
- 2=$15,000 to $35,999
- 3=$36,000 to $45,999
- 4=$46,000 to $65,999
- 5=$66,000 to $99,999
- 6=$100,000 to $149,999
- 7=$150,000 to $199,999
- 8= $200,000 or more
g7 <- as.factor(d[,"g7"])
# Make "*" to NA
g7[which(g7=="*")]<-"NA"
levels(g7) <- list(Less_than_15000="1",
Between_15000_35999="2",
Between_36000_45999="3",
Between_46000_65999="4",
Between_66000_99999="5",
Between_100000_149999= "6",
Between_150000_199999="7",
More_than_200000="8")
g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))
new.d <- data.frame(new.d, g7)
new.d <- apply_labels(new.d, g7 = "income")
temp.d <- data.frame (new.d, g7)
result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g7")
g7
| Less_than_15000 |
756 |
12.9 |
14.0 |
12.9 |
14.0 |
| Between_15000_35999 |
967 |
16.6 |
17.9 |
29.5 |
31.9 |
| Between_36000_45999 |
569 |
9.7 |
10.5 |
39.3 |
42.5 |
| Between_46000_65999 |
776 |
13.3 |
14.4 |
52.5 |
56.8 |
| Between_66000_99999 |
968 |
16.6 |
17.9 |
69.1 |
74.8 |
| Between_100000_149999 |
753 |
12.9 |
14.0 |
82.0 |
88.7 |
| Between_150000_199999 |
348 |
6.0 |
6.4 |
88.0 |
95.2 |
| More_than_200000 |
260 |
4.5 |
4.8 |
92.4 |
100.0 |
| NA |
442 |
7.6 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
G8: # people supported by income
- G8. In the last calendar year, how many people, including yourself, were supported by your family income?
- 1=1
- 2=2
- 3=3
- 4=4
- 5=5 or more
g8 <- as.factor(d[,"g8"])
# Make "*" to NA
g8[which(g8=="*")]<-"NA"
levels(g8) <- list(One="1",
Two="2",
Three="3",
Four="4",
Five_or_more="5")
g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))
new.d <- data.frame(new.d, g8)
new.d <- apply_labels(new.d, g8 = "people supported by income")
temp.d <- data.frame (new.d, g8)
result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g8")
g8
| One |
1696 |
29.0 |
30.5 |
29.0 |
30.5 |
| Two |
2464 |
42.2 |
44.3 |
71.2 |
74.7 |
| Three |
746 |
12.8 |
13.4 |
84.0 |
88.1 |
| Four |
399 |
6.8 |
7.2 |
90.9 |
95.3 |
| Five_or_more |
261 |
4.5 |
4.7 |
95.3 |
100.0 |
| NA |
273 |
4.7 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
G9: Worry about finance
- G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
- During young adult life (up to age 30):
- Age 31 (up to just before prostate cancer diagnosis):
- Current (from prostate cancer diagnosis to present):
- 1=Not at all worried
- 2=A little worried
- 3=Somewhat worried
- 4=Very worried
g9a <- as.factor(d[,"g9a"])
# Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
levels(g9a) <- list(Not_worried="1",
A_little_worried="2",
Somewhat_worried="3",
Very_worried="4")
new.d <- data.frame(new.d, g9a)
new.d <- apply_labels(new.d, g9a = "young adult life")
temp.d <- data.frame (new.d, g9a)
result<-questionr::freq(temp.d$g9a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
| Not_worried |
2684 |
46.0 |
47.4 |
| A_little_worried |
1474 |
25.2 |
26.0 |
| Somewhat_worried |
990 |
17.0 |
17.5 |
| Very_worried |
512 |
8.8 |
9.0 |
| NA |
179 |
3.1 |
NA |
| Total |
5839 |
100.0 |
100.0 |
g9b <- as.factor(d[,"g9b"])
# Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
levels(g9b) <- list(Not_worried="1",
A_little_worried="2",
Somewhat_worried="3",
Very_worried="4")
new.d <- data.frame(new.d, g9b)
new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
temp.d <- data.frame (new.d, g9b)
result<-questionr::freq(temp.d$g9b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
| Not_worried |
2824 |
48.4 |
51.2 |
| A_little_worried |
1504 |
25.8 |
27.3 |
| Somewhat_worried |
855 |
14.6 |
15.5 |
| Very_worried |
333 |
5.7 |
6.0 |
| NA |
323 |
5.5 |
NA |
| Total |
5839 |
100.0 |
100.0 |
g9c <- as.factor(d[,"g9c"])
# Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
levels(g9c) <- list(Not_worried="1",
A_little_worried="2",
Somewhat_worried="3",
Very_worried="4")
new.d <- data.frame(new.d, g9c)
new.d <- apply_labels(new.d, g9c = "current")
temp.d <- data.frame (new.d, g9c)
result<-questionr::freq(temp.d$g9c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
| Not_worried |
3158 |
54.1 |
56.7 |
| A_little_worried |
1122 |
19.2 |
20.2 |
| Somewhat_worried |
756 |
12.9 |
13.6 |
| Very_worried |
530 |
9.1 |
9.5 |
| NA |
273 |
4.7 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G10:Own or rent a house
- G10. Is the home you live in:
- 1=Owned or being bought by you (or someone in the household)?
- 2=Rented for money?
- 3=Other
g10 <- as.factor(d[,"g10"])
# Make "*" to NA
g10[which(g10=="*")]<-"NA"
levels(g10) <- list(Owned="1",
Rented="2",
Other="3")
g10 <- ordered(g10, c("Owned","Rented","Other"))
new.d <- data.frame(new.d, g10)
new.d <- apply_labels(new.d, g10 = "Own or rent a house")
temp.d <- data.frame (new.d, g10)
result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g10")
g10
| Owned |
4178 |
71.6 |
74.1 |
71.6 |
74.1 |
| Rented |
1310 |
22.4 |
23.2 |
94.0 |
97.3 |
| Other |
151 |
2.6 |
2.7 |
96.6 |
100.0 |
| NA |
200 |
3.4 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
G10 Other: Own or rent a house
g10other <- d[,"g10other"]
new.d <- data.frame(new.d, g10other)
new.d <- apply_labels(new.d, g10other = "g10other")
temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G10 Other")
G10 Other
| #NAME? |
1 |
0.0 |
0.5 |
| 700 |
1 |
0.0 |
0.5 |
| A friend |
1 |
0.0 |
0.5 |
| Agency |
1 |
0.0 |
0.5 |
| Air property family owned |
1 |
0.0 |
0.5 |
| Apartment |
3 |
0.1 |
1.4 |
| Apartment manager |
1 |
0.0 |
0.5 |
| Apartment rent |
1 |
0.0 |
0.5 |
| Apartment renting |
1 |
0.0 |
0.5 |
| Apartment. |
4 |
0.1 |
1.9 |
| Apt |
1 |
0.0 |
0.5 |
| Apt building |
1 |
0.0 |
0.5 |
| Apt.. |
1 |
0.0 |
0.5 |
| Assisted living |
1 |
0.0 |
0.5 |
| Bought. |
1 |
0.0 |
0.5 |
| budget inn |
1 |
0.0 |
0.5 |
| Buying |
1 |
0.0 |
0.5 |
| co-op |
1 |
0.0 |
0.5 |
| Cooperative |
1 |
0.0 |
0.5 |
| Currently in a nursing facility. |
1 |
0.0 |
0.5 |
| Currently live in a trailer on family land |
1 |
0.0 |
0.5 |
| Currently looking to move to apartment/townhouse/rent. |
1 |
0.0 |
0.5 |
| Daughter |
1 |
0.0 |
0.5 |
| Estate |
1 |
0.0 |
0.5 |
| Family |
1 |
0.0 |
0.5 |
| Family estate |
2 |
0.0 |
1.0 |
| Family home |
5 |
0.1 |
2.4 |
| Family home, pay rent. |
1 |
0.0 |
0.5 |
| Family home. |
2 |
0.0 |
1.0 |
| Family house |
2 |
0.0 |
1.0 |
| Family owned |
1 |
0.0 |
0.5 |
| Family property |
3 |
0.1 |
1.4 |
| Foreclosure |
1 |
0.0 |
0.5 |
| Foreclosure. |
1 |
0.0 |
0.5 |
| Free rent for now - friend. |
1 |
0.0 |
0.5 |
| Friend who he lives with owns it and he pays rent |
1 |
0.0 |
0.5 |
| Have a landlord |
1 |
0.0 |
0.5 |
| His house |
1 |
0.0 |
0.5 |
| Homeless |
3 |
0.1 |
1.4 |
| House belongs to my wife, Mrs. —- this home is permanent. Thank God. |
1 |
0.0 |
0.5 |
| House fire necessitated a rental home till home is repaired. |
1 |
0.0 |
0.5 |
| House sitter |
1 |
0.0 |
0.5 |
| HUD |
1 |
0.0 |
0.5 |
| I currently rent an apartment |
1 |
0.0 |
0.5 |
| I have a room in this house |
1 |
0.0 |
0.5 |
| I live in a RV park on the street |
1 |
0.0 |
0.5 |
| I live on Social Security only |
1 |
0.0 |
0.5 |
| I live with a friend |
1 |
0.0 |
0.5 |
| I live with a friend who I help out. |
1 |
0.0 |
0.5 |
| I live with my mother in her home. |
1 |
0.0 |
0.5 |
| I own |
1 |
0.0 |
0.5 |
| I owned the house |
1 |
0.0 |
0.5 |
| I pay rent |
2 |
0.0 |
1.0 |
| I pay rent for a room |
1 |
0.0 |
0.5 |
| I pay rent where I live |
1 |
0.0 |
0.5 |
| I rent |
2 |
0.0 |
1.0 |
| I rent (pay rent) |
1 |
0.0 |
0.5 |
| I rent a furnished room and kitchen priv. |
1 |
0.0 |
0.5 |
| I rent an apartment |
1 |
0.0 |
0.5 |
| I rent an apt.. |
1 |
0.0 |
0.5 |
| I rent here in Los Angeles and own a home in Neveda |
1 |
0.0 |
0.5 |
| I rent home from daughter. |
1 |
0.0 |
0.5 |
| I rent my house from owner. |
1 |
0.0 |
0.5 |
| I rent the home |
1 |
0.0 |
0.5 |
| I rent. |
2 |
0.0 |
1.0 |
| I rented |
1 |
0.0 |
0.5 |
| I worked and live on property for my boss. |
1 |
0.0 |
0.5 |
| I’m renting an apartment |
1 |
0.0 |
0.5 |
| If you need to call me Larry D. Joyner 912-344-5895. |
1 |
0.0 |
0.5 |
| Inherit it. |
1 |
0.0 |
0.5 |
| Inherited |
1 |
0.0 |
0.5 |
| Inherited. |
1 |
0.0 |
0.5 |
| It was paid for with Harvey and Borrowed from SBA to repair |
1 |
0.0 |
0.5 |
| James Williams. |
1 |
0.0 |
0.5 |
| Landlord |
1 |
0.0 |
0.5 |
| Lease |
1 |
0.0 |
0.5 |
| Leased |
1 |
0.0 |
0.5 |
| Live assisted living |
1 |
0.0 |
0.5 |
| Live in an apartment |
1 |
0.0 |
0.5 |
| Live in apartment community |
1 |
0.0 |
0.5 |
| Live in apartment complex |
1 |
0.0 |
0.5 |
| Live in apartment pay rent |
1 |
0.0 |
0.5 |
| Live in low income. |
1 |
0.0 |
0.5 |
| Live in my brothers house. |
1 |
0.0 |
0.5 |
| Live with family. |
1 |
0.0 |
0.5 |
| Live with mother in law |
1 |
0.0 |
0.5 |
| Live with parents |
1 |
0.0 |
0.5 |
| living in car/homeless |
1 |
0.0 |
0.5 |
| Living with a family member. |
1 |
0.0 |
0.5 |
| Living with cousin’s home |
1 |
0.0 |
0.5 |
| Living with uncle |
1 |
0.0 |
0.5 |
| Lost home in tax sale. |
1 |
0.0 |
0.5 |
| Low income senior citizens |
1 |
0.0 |
0.5 |
| Mobile Home Camper |
1 |
0.0 |
0.5 |
| Mortgage |
4 |
0.1 |
1.9 |
| Mortgage. |
1 |
0.0 |
0.5 |
| Mother |
1 |
0.0 |
0.5 |
| Mother. |
1 |
0.0 |
0.5 |
| My mothers |
1 |
0.0 |
0.5 |
| My sister’s house. |
1 |
0.0 |
0.5 |
| My sisters home |
1 |
0.0 |
0.5 |
| My wife and myself owned. |
1 |
0.0 |
0.5 |
| No mortgage |
1 |
0.0 |
0.5 |
| No my home |
1 |
0.0 |
0.5 |
| Nursing home |
1 |
0.0 |
0.5 |
| Own |
2 |
0.0 |
1.0 |
| Owned |
3 |
0.1 |
1.4 |
| Owned —- out to 1985, zero love dollars and affection |
1 |
0.0 |
0.5 |
| Owned by mother. |
1 |
0.0 |
0.5 |
| Owned by my employer |
1 |
0.0 |
0.5 |
| Owned by my wife |
1 |
0.0 |
0.5 |
| Owned by parent, paying rent, car note |
1 |
0.0 |
0.5 |
| owned by sister |
1 |
0.0 |
0.5 |
| Owned by someone in household. |
1 |
0.0 |
0.5 |
| Owned by Step daughter |
1 |
0.0 |
0.5 |
| Owned house |
1 |
0.0 |
0.5 |
| Owned. |
2 |
0.0 |
1.0 |
| Paid for |
2 |
0.0 |
1.0 |
| Paid in full. |
1 |
0.0 |
0.5 |
| Passed down to me |
1 |
0.0 |
0.5 |
| Pay association fees |
1 |
0.0 |
0.5 |
| Pay for! |
1 |
0.0 |
0.5 |
| Pay rent. |
1 |
0.0 |
0.5 |
| Paying mortgage |
1 |
0.0 |
0.5 |
| Paying mortgage. |
1 |
0.0 |
0.5 |
| Paying rent |
1 |
0.0 |
0.5 |
| Provided by my employer |
1 |
0.0 |
0.5 |
| Public housing. |
1 |
0.0 |
0.5 |
| Pvt owned |
1 |
0.0 |
0.5 |
| Rent |
6 |
0.1 |
2.9 |
| Rent a room |
2 |
0.0 |
1.0 |
| Rent an apartment |
1 |
0.0 |
0.5 |
| Rent apt |
1 |
0.0 |
0.5 |
| Rent apt.. |
1 |
0.0 |
0.5 |
| Rent from ex wife |
1 |
0.0 |
0.5 |
| Rent I pay 200 month |
1 |
0.0 |
0.5 |
| Rent room |
1 |
0.0 |
0.5 |
| Rent space. |
1 |
0.0 |
0.5 |
| Rent. |
2 |
0.0 |
1.0 |
| Rental |
1 |
0.0 |
0.5 |
| rental - apartment |
1 |
0.0 |
0.5 |
| rented |
1 |
0.0 |
0.5 |
| Rented |
2 |
0.0 |
1.0 |
| Rented home |
1 |
0.0 |
0.5 |
| renter |
1 |
0.0 |
0.5 |
| Renting |
2 |
0.0 |
1.0 |
| Renting a home |
1 |
0.0 |
0.5 |
| Reverse mortgage |
2 |
0.0 |
1.0 |
| Rooming |
1 |
0.0 |
0.5 |
| Roommate situation. |
1 |
0.0 |
0.5 |
| RV |
1 |
0.0 |
0.5 |
| Senior apt |
1 |
0.0 |
0.5 |
| Senior citizen building (Brookside Apartments). |
1 |
0.0 |
0.5 |
| Senior citizen housing |
1 |
0.0 |
0.5 |
| Senior facility. |
1 |
0.0 |
0.5 |
| Senior living qtr.. |
1 |
0.0 |
0.5 |
| Senior Public Housing |
1 |
0.0 |
0.5 |
| Seniors home |
1 |
0.0 |
0.5 |
| Sisters house |
1 |
0.0 |
0.5 |
| Son owns the home-I pay the mortgage. |
1 |
0.0 |
0.5 |
| Squatted. |
1 |
0.0 |
0.5 |
| Staying with a friend. |
1 |
0.0 |
0.5 |
| Staying with my daughter and family |
1 |
0.0 |
0.5 |
| Staying with relative who owns the home |
1 |
0.0 |
0.5 |
| This house is hers-not my house. Renter. |
1 |
0.0 |
0.5 |
| Trailer-living with family. |
1 |
0.0 |
0.5 |
| we are selling home |
1 |
0.0 |
0.5 |
| Wife |
2 |
0.0 |
1.0 |
| Wife family home. |
1 |
0.0 |
0.5 |
| Work compensation |
1 |
0.0 |
0.5 |
| work for the us government |
1 |
0.0 |
0.5 |
| NA |
5630 |
96.4 |
NA |
| Total |
5839 |
100.0 |
100.0 |
G11:Lose current sources
- G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
- 1=Less than 1 month
- 2=1 to 2 months
- 3=3 to 6 months
- 4=More than 6 months
g11 <- as.factor(d[,"g11"])
# Make "*" to NA
g11[which(g11=="*")]<-"NA"
levels(g11) <- list(Less_than_1_month="1",
One_to_two_month="2",
Three_to_six_month="3",
More_than_6_months="4")
g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))
new.d <- data.frame(new.d, g11)
new.d <- apply_labels(new.d, g11 = "ose current sources")
temp.d <- data.frame (new.d, g11)
result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g11")
g11
| Less_than_1_month |
688 |
11.8 |
12.5 |
11.8 |
12.5 |
| One_to_two_month |
1083 |
18.5 |
19.7 |
30.3 |
32.2 |
| Three_to_six_month |
1201 |
20.6 |
21.8 |
50.9 |
54.1 |
| More_than_6_months |
2525 |
43.2 |
45.9 |
94.1 |
100.0 |
| NA |
342 |
5.9 |
NA |
100.0 |
NA |
| Total |
5839 |
100.0 |
100.0 |
100.0 |
100.0 |
G12: Today’s date
- G12. Please enter today’s date.
g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
new.d <- data.frame(new.d, g12)
new.d <- apply_labels(new.d, g12 = "today’s date")
#temp.d <- data.frame (new.d.1, g12)
summarytools::view(dfSummary(new.d$g12, style = 'grid',
max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
g12
[labelled, Date] |
today’s date |
| min : 1980-08-23 | | med : 2020-06-25 | | max : 2040-11-14 | | range : 60y 2m 22d |
|
402 distinct values |
 |
79
(1.4%) |
Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09